Luminance vs Robin AI: how they compare in 2026
The gap between these two is wide, and it is worth understanding why before treating it as a verdict on the products. Almost every point of separation is a document Luminance publishes and Robin AI does not. Luminance publishes a benchmark built on 189,000 annotated data points, gives every customer a dedicated single tenant instance with no data co mingling, and documents access control down to the fact that its own staff cannot view customer documents. Robin AI publishes real controls too, but its training commitment is conditional. It says customer data will not be used for training without express consent, which is a permission you can grant rather than a prohibition it has accepted. For a confidentiality review, that is the sentence that matters.
At a glance
All 15 axes, side by side
The same grid applied to every vendor in the index, graded from public sources. Hover a grade to see what the letter means on that axis.
AI Centrality
How much of the product is actually AI. Whether the machine learning is the mechanism the buyer is paying for or a feature layered onto conventional software, and whether the vendor is specific about which is which.
The artificial intelligence is the product, and the vendor owns much of the stack. Founded by mathematicians in 2015, it runs a multi model architecture it calls a Panel of Judges, combining foundation, fine tuned and proprietary models. It ships its own legal intelligence model, Luna Crescent, trained in house and deployed in its own environment. Remove the models and there is no product.
The models are the product. Review, redlining, the Word add in's Ask, Draft, Edit and Research modes, clause level risk analysis and repository search are all generative or machine learning capabilities. The vendor states a training base of more than 4.5 million legal documents and 100 million legal clauses. One qualification applies: the company also sells Managed Services combining the software with human legal review, so part of what it sells is people. The software product itself is model driven throughout.
Citation Accuracy and Hallucination Disclosure
Whether the vendor publishes measured accuracy on citations and assertions, grounds output to primary sources, and says plainly what its system does when it does not know. Legal has a documented public record of fabricated citations reaching filed briefs, so an untested claim of accuracy is not evidence.
Substantive disclosure with a real gap. The vendor publishes ContractIQ Bench, a proprietary benchmark of 189,000 manually annotated and reviewed data points assessing interpretation of named provision types, including liability caps, termination for convenience and confidentiality obligations. It is tested on held out documents and concepts excluded from training, with blind evaluations by legal experts alongside. It publishes a result, 5 percent higher accuracy than leading general purpose models on contract understanding, and a speed figure of 200 to 400 tokens per second. It also states that the model is trained to prioritize faithful extraction and to identify absence rather than invent an answer. Two gaps remain. The published figure is a relative difference with no absolute accuracy rate and no named comparator models, so a buyer cannot tell what the 5 percent is measured against. And the benchmark is proprietary, with no sample tasks or rubric published, so an outsider cannot inspect or run it again.
Accuracy is asserted without measurement. Grounding exists in outline. The Word add in's Research mode is described as enhancing answers with authoritative, current information from a curated set of trusted legal sources on the web, and review output is clause level with risk flags tied to company standards. No accuracy figure, hallucination rate, test set or evaluation method is published, and nothing describes how retrieval reaches those trusted sources or how a reader opens and verifies one. The headline claim across vendor material is a speed figure, 80 to 85 percent faster review, rather than an accuracy figure.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
A real published commitment with a described control mechanism, short of the full structure. The Panel of Judges architecture is itself an oversight design and is documented: multiple models analyze each clause independently and reach consensus, which the vendor states reduces hallucination risk. Outputs are described as traceable, and Traffic Light Analysis ranks deviation risk visually so a reviewer sees where to look. The vendor publishes a position piece arguing that human in the loop alone is insufficient and that systems must be designed for accuracy and transparency. Not located: where the review point sits when the product negotiates with a counterparty directly, the threshold at which it escalates to a lawyer, and what the vendor commits to when an output is wrong. The first gap matters because the product sends agreements to counterparties and negotiates on the customer's behalf.
A real published commitment has the models working alongside a supervising lawyer, with documented review surfaces and no full control structure. The Word add in surfaces clause level recommendations as tracked changes, comments or highlighted text based on company standards, which a lawyer accepts or rejects in place. The four modes separate asking from drafting from editing, so the user chooses the level of intervention. The Managed Services line puts the company's own legal professionals in the loop as a paid option, a literal form of oversight. Not located: any threshold at which the system defers, what it does on its own, and what the vendor commits to when an output is wrong.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
Real deployment evidence with substance, short of attribution and method. Named customers appear in vendor and trade material including Hitachi, AMD, BBC Studios, Yokogawa and Koch, alongside a stated base of more than 700 organizations across 70 plus countries and all four of the Big Four consultancies. A customers page is published. The recurring figure, negotiation time reduced by up to 90 percent, is a vendor claim carrying a hedge and is not tied to any named customer, dated, or accompanied by a method. Searched the site, the customers page, the press releases and the resources index and located no case study pairing a named organization with figures and a date.
Customer logos and press mentions stand in for evidence, with results quoted from an internal source. A customers page exists and press logos including the Financial Times, Bloomberg, Forbes and CNBC are displayed prominently, alongside investor logos including Google, Temasek and PayPal. The most specific published figure, review completed over 85 percent faster, is attributed to the vendor's own internal legal team testing its own add in rather than to a customer. Searched the site, the customers page, the news and blog indexes and the guides and reports index and located no named customer deployment carrying figures, a date and an assessable method. Press coverage and investor backing are not deployment evidence.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
Substantive published commitments, short of the full picture. Segregation is the strongest element and is documented precisely: each customer receives a dedicated single tenant instance with complete isolation and no mixing of data. Access control is documented in depth, including that vendor staff cannot view customer documents without explicit authorization given through the user interface, with all access tracked and audited. Encryption is specified to the key management service, cipher and rotation practice. Two gaps remain. No prohibition on training with customer content is published anywhere in vendor material, which stands out for a vendor that trains its own models and publishes a figure of 220 million legal documents. Attorney client privilege and work product handling is not addressed directly.
Confidentiality is asserted in general terms, and the one commitment that matters carries a qualifier that changes it. The security page states that customer data will not be used for model training, fine tuning or other feature development without express consent. That is permission gated by consent rather than a prohibition. Real controls are published: AES-256 at rest, current TLS in transit, and a statement that data never leaves the vendor's AWS environment. Not located: handling of attorney client privilege or work product, any segregation model between customers, users or matters, and any retention or deletion terms. For a product whose core use is taking in counterparty contracts at volume, the absence of a segregation statement is material.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
A boilerplate structure sits in the terms while the product is sold well beyond lawyers. Dedicated solution pages target compliance, executive, sales, procurement, finance, human resources and marketing teams alongside legal, and the product negotiates contracts on a customer's behalf. Searched the site, the solution pages, the published terms and conditions, the privacy policy and the resources index on 29 Aug 2026. No position on advice versus tooling, no treatment of competence or supervision duties, and no statement of jurisdiction limits is published, despite operation in more than 70 countries. The tool is marketed to marketing and HR departments.
The audience is lawyers and legal teams, and the terms carry a standard structure, but no position on the advice line is published. The site, the platform and services pages, the published terms and the privacy policy, searched, showed no statement on advice versus tooling, no treatment of competence or supervision duties, and no jurisdiction limits. That is despite the company operating across the UK, US and Singapore and marketing to multiple jurisdictions. The Managed Services line, in which the vendor's own legal professionals review work for customers, raises the advice question more sharply than a pure software product would, and nothing published says how that service is scoped or supervised.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
Principles and architecture are published without a governance mechanism a buyer could audit. What exists is real and substantial: a published white paper on how the AI is built, a named Director of AI who writes technical material under his own name, and a research team based in Cambridge. There is also a described validation regime through ContractIQ Bench, and a security advisory board of named external experts. That board governs security rather than model behavior, and no equivalent exists for AI governance. Not located: a named owner of model governance, a testing gate before release as distinct from benchmark results, an AI management certification such as ISO 42001, or anything on uneven output across matter types, parties or populations.
No governance position for model behavior is published: no named internal owner, no pre release testing regime, no published responsible AI or AI governance framework, no AI specific certification such as ISO 42001, and nothing on uneven output across matter types, parties or populations. The vendor publishes real security governance, which is a different subject.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
Substantive published policy covers most of the ground in specific detail. The security FAQ describes AWS Key Management Service encryption at S3 and EC2 level with AES-256 keys rotated regularly, TLS 1.2 or higher in transit, and dedicated single tenant instances. It describes role based and division level permissions configured by the customer under least privilege, configurable password and session timeout policy, mandatory staff security training, threat detection through Darktrace's Enterprise Immune System and Juniper firewalls, and an incident management process covering detection, mitigation and communication. Backups are stated precisely: nightly to a secondary AWS data center in the same region, encrypted, and kept at least 14 days. Not located: a retention period or deletion control for customer documents in normal operation as distinct from backups, and a named subprocessor list.
A generic security posture covers the product without addressing what happens to documents and prompts after processing. Published and real: AES-256 at rest, current TLS in transit, security built into the development lifecycle, data replication across geographic locations with disaster recovery exercises and automated failover, and named monitoring tools in AWS CloudWatch, CloudTrail and Datadog. That is operational security described in useful detail. Not located: any retention period for documents or prompts, any deletion control, a named subprocessor list, and an incident or breach notification practice. Anthropic and AWS are named as partners on the security page, which partly discloses the supply chain but is not a subprocessor list.
AI Liability and Recourse
What the vendor stands behind contractually when its output is wrong. Indemnities, caps, carve outs, insurance, and whether any of it is published or only reachable through a negotiated agreement.
Liability is addressed only through published terms carrying a standard structure. Terms and conditions are published openly alongside a privacy policy, cookie policy and anti slavery statement, so a buyer can read the allocation of loss before entering a sales process, which keeps this above a pure absence. Searched those documents, the security page and the security standards white paper entry point and located no indemnity running to the customer for third party claims arising from output, no warranty on output, no stated liability cap figure and no insurance position. Worth noting the product negotiates with counterparties on a customer's behalf, which raises the recourse question more sharply than a review only tool.
Liability is addressed only through published terms carrying a standard limitation structure. Terms and a privacy policy are published openly, so a buyer can read the allocation of loss before entering a sales process, which keeps this above a pure absence. Searched those documents, the security page and the trust center entry point and located no indemnity running to the customer for third party claims arising from output, no warranty on output, no stated liability cap figure and no insurance position. Worth noting that the Managed Services line involves the vendor performing legal review work, which raises a distinct recourse question that published material does not address.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
Integrations are asserted at platform level with no documentation an implementer could use. The vendor describes connecting intake, negotiation, workflow and repository intelligence in one platform, and a Collaborate product exists for working with counterparties, and third party sources reference Microsoft Word and Outlook working surfaces. What is not published on the vendor's own property, after checking the platform pages, the technology page, the security page and the resources index, is any integrations page, any named connector for document management, contract lifecycle, e signature, CRM or ERP, and any description of what an integration moves or what an administrator configures. For an enterprise platform sold to procurement and finance functions, that absence is notable.
Integrations are claimed without documentation an implementer could use. The Microsoft Word add in is real, documented at user level in Robin University, and is the primary surface, alongside a browser application with a searchable executed contract repository. Beyond Word, third party sources describe CRM and CLM integration but no vendor integrations page is published, and no named connector for document management, contract lifecycle or e signature is published on the property. A third party review notes limited file format support beyond Word documents. Nothing published describes what any integration moves, in which direction, or what an administrator configures.
Deployment Model and Data Residency
Where the software runs and where the data sits. Multi tenant cloud, single tenant, private deployment, on premises, and whether region of residence is a published option or an enterprise conversation.
The deployment model is stated clearly, with partial residency detail. Three things are published and specific: a dedicated single tenant AWS instance per customer with complete isolation, deployment within the customer's own environment as an alternative to the hosted option, and backup to a secondary AWS data center within the same region, which confirms data stays in region. The vendor states AWS global infrastructure provides a solution tailored to geographic requirements. What is missing is the list: no named available regions, no statement of which regions a customer may select, and no statement of where processing happens as distinct from where data is stored.
Cloud delivery is stated, and neither the tenancy model nor the region is. The vendor says data never leaves its AWS environment and is replicated across multiple geographic locations for disaster recovery. That is a resilience statement rather than a residency one, and it arguably cuts against a customer wanting data confined to one jurisdiction. Not located: any named region, any residency the customer can choose, any tenancy model, and any statement of where processing happens as distinct from storage. Other sources mention private cloud deployment options, which is not vendor material. For a UK founded vendor selling into the UK, US and Singapore, published residency options would be expected.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
Certification is real and stated with correct nouns, short of accessible evidence. ISO 27001:2022 is named with its version and described as certification. The SOC 2 Type 2 language is precise: the vendor says it successfully completed a SOC 2 Type 2 examination assessing controls related to security, availability and confidentiality, which names the trust services criteria in scope. Regular independent third party penetration testing is stated. A named external security advisory board, including a former Director General of MI5 and two former Darktrace executives, is published with full biographies. What is missing is the evidence route: no trust portal is published, no report is downloadable or requestable through a published flow, and no coverage period, report date or auditor name is published as of 29 Aug 2026. A security standards white paper is published, the nearest thing to an evidence route.
Certification is real and stated with an open access route, without scope or evidence. ISO 27001 and SOC 2 are both named as certified and GDPR compliance is stated. A trust center is published at a stable URL as a self serve route rather than a sales gate. Not located: any coverage period, audit scope, report date, SOC 2 type or named auditing firm. The security page writes ISO without a number in one place and ISO27001 in another. The certification is stated clearly enough elsewhere that this reads as loose copy rather than a problem with the claim.
Model Supply Chain Disclosure
Which models sit underneath, whose they are, where they run, and whether the vendor commits to telling customers when that changes. A legal buyer inherits every dependency it cannot see.
The supply chain is partly disclosed and the architecture is described in depth. The vendor publishes that it runs a multi model Panel of Judges combining foundation, fine tuned and proprietary models, that it continuously evaluates and selects the best model for each task, and that its own model Luna Crescent is deployed inside its own AWS environment. It states the consequence in the customer's terms: owning the model reduces reliance on external model providers, limits data exposure to additional subprocessors, and protects customers from third party availability, pricing and access disruption. Hosting is named as AWS. Not published is which foundation models sit in the panel, from which providers, or which tasks route to them, and no subprocessor list or change notification commitment is published. A buyer knows the shape of the chain and its own model, but not the third party links in it.
The vendor names a partner without disclosing the supply chain. Anthropic and AWS are both named as partners on the security page, and other coverage describes collaboration with Anthropic and AWS to build secure models, so a reader can infer the likely model provider. But naming a partnership is not the same as stating which model serves which task. Not located: which models are used in production, where they run, whether any other provider is involved, a subprocessor list, or any commitment to notify customers when the supply chain changes. The partner logo invites an inference that the published material does not confirm.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
No pricing page exists on the property, no rate is published, no unit of charge is stated and no tier structure appears. Every commercial path terminates in a demo request. No free trial or self serve entry point is published, and no third party pricing figure is published either.
No pricing page exists, no rate is published, no unit of charge is stated and no tier structure appears. Every commercial path ends in a demo request. Vendor material refers to a free account with add in access and paid accounts with playbook customization, and tells buyers to speak to a member of the team to learn what paid accounts include, so even the split of features between free and paid is not published. Other sources describe subscription pricing without figures.
Firm and Practice Coverage
Who the product is actually built for. AmLaw, midlaw, small firm and solo, in house departments, government and courts, and which practice areas are supported rather than merely claimed.
Who the product serves is documented precisely across two dimensions, each with its own published pages. Six industries: manufacturing, financial services, pharmaceutical, technology, insurance and chemical. Eight business functions: legal, compliance, executive, sales, procurement, finance, human resources and marketing. Both law firms and corporate legal departments are addressed, with a stated base of more than 700 organizations across more than 70 countries including all four Big Four consultancies and named enterprises. An academic program is published as a separate segment. The practice boundary is clear from the structure and consistent throughout: this is contract work end to end, from generation through negotiation to post execution analysis and investigation, and nothing on the property claims litigation or research capability it does not have.
Coverage is claimed broadly without evidence that the breadth is real. Vendor material addresses in house legal teams, law firms and corporate clients across multiple jurisdictions. The practice focus is clearly contract work: review, drafting, negotiation and tracking obligations after signature, with other sources naming NDAs, service agreements, procurement and employment agreements. Offices are published in London, New York and Singapore. No segmentation a buyer could use is published. There are no pages by firm size, industry or role, no statement of which practice areas or team sizes the product is built for, and no statement of what it is not built for. The customers page exists but does not resolve the segments claimed.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
Public material states that customer content from the platform trains the model. The vendor's press release of 17 February 2022 says its AI has been exposed to more than 100 million documents and is also learning from the interactions between humans and the documents. It says the AI learns from every NDA or supplier agreement negotiated within Luminance, every clause that causes an M&A transaction to fall apart and every piece of data culled during eDiscovery.
The May 2023 Ask Lumi release repeats that the Legal Pre-Trained Transformer learns solely from legally verified documents, now put at more than 150 million. No aggregation, anonymization or deidentification qualifier is stated, and nothing published says whether a customer can decline or whether learning from one customer's negotiations stays with that customer. The published terms and conditions and privacy policy carry no matching term in either direction.
The statement is dated, and the vendor's current pages describe the training data without repeating the sentence about learning from customer negotiations; that reflects age, not a withdrawal. A vendor statement of what its model learned from is a stated position rather than silence.
The security page states that customer data will not be used for model training, fine tuning or other feature development without express consent. Read precisely, that is a commitment not to train without consent rather than a prohibition, so training becomes possible where consent is given. No material was located describing how consent is sought, at what level it is given, or whether it is a contract term or a product setting.
The vendor's trust center separately states that data is never used to train other AI models, which addresses outside models rather than its own. The qualifier and the ambiguity are stated here and left unresolved.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Retention is acknowledged and partly quantified without a period for the primary system. The security FAQ states each customer instance is backed up nightly to a secondary AWS data center in the same region, encrypted, and kept for a minimum of 14 days, which is a real published figure and a floor a buyer can plan against. What was not located as of 29 Aug 2026 is any retention period for documents, prompts or outputs in normal operation, any customer control over that window, or any deletion commitment. A minimum backup retention states how long data persists after deletion rather than how long it is held.
Searched the security page, the trust center entry point, the published terms, the privacy policy and the help center entry point on 29 Aug 2026. No public material states how long documents, prompts or outputs are retained, whether a customer controls the window, or whether deletion is available. The vendor does publish that data never leaves its AWS environment and that data is replicated across multiple geographic locations for disaster recovery, which describes where copies sit rather than for how long.
The browser application maintains a searchable repository of executed contracts, so the product is designed to retain documents, with no published terms attached.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
The product maintains its own documented permission model. Between customers, isolation is architectural: each has a dedicated single tenant instance, stated to ensure complete isolation with no mixing of data. Inside a customer, the vendor publishes division level permissions administered by the customer under least privilege, role based configuration, and customer configurable password and session timeout policy. Vendor staff cannot view customer documents without explicit authorization given through the interface, with all access tracked and audited.
Division level permissions are the nearest published equivalent to a wall. No document management integration was located whose permissions retrieval could inherit at query time, and conflicts and ethical walls are not addressed as such.
Searched the security page, the trust center entry point, the platform pages, the published terms and the help center entry point on 29 Aug 2026. No vendor material addresses segregation of any kind: not between customers, not between users, not between matters. Third party sources refer to sophisticated user permissions and single sign on, which is not vendor material and was not treated as evidence. No document management integration was located whose permissions retrieval could inherit.
The product ingests counterparty contracts at volume and maintains a shared searchable repository, which makes the absence of any published segregation model a live question rather than an academic one.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Searched the published terms and conditions, the privacy policy, the security page and its FAQ, and the security standards white paper entry point on 29 Aug 2026. No clause committing to notify a customer of a government or law enforcement request for their data was located, and no transparency report was located. Noted for a future reader: the single tenant and on premises deployment options materially change what a vendor could produce in response to such a request, but the vendor does not make that argument in published material and it is not recorded as a value here.
Searched the published terms, the privacy policy, the security page and the trust center entry point on 29 Aug 2026. No clause committing to notify a customer of a government or law enforcement request for their data was located, and no transparency report was located. This records a search across the published documents that did not surface the clause rather than a reading of every document end to end.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
The training data is contract and legal document data behind a proprietary model rather than primary law, and it is quantified in detail without being sourced. Published: more than 220 million verified legal documents the platform has been exposed to over a decade, and a curated training selection of roughly 3.4 million legal concepts and data points. Its breadth is described as spanning virtually every industry, including complex agreements, difficult file formats and obscure drafting styles.
A separate benchmark set has 189,000 manually annotated data points. Not published is where any of it came from or on what rights basis it was assembled. Trade coverage notes that many documents in the training base were not publicly disclosed. Scale and character are described, while the source and license basis are not.
Two bodies of content are described and neither is identified. The training base is quantified as more than 4.5 million legal documents and 100 million legal clauses, with no statement of where those documents came from or on what rights basis they were assembled. That matters more for a collection of contracts than for public case law. Separately, the Word add in's Research mode is described as drawing on a curated set of trusted legal sources on the web, with none of those sources named. No license basis, jurisdiction list or update schedule was located for either as of 29 Aug 2026.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
Searched the site, the six platform product pages, the technology page and the resources index on 29 Aug 2026. No material was located addressing whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator license was located. Noted for context: this is a contract lifecycle product whose grounding is contract language and the customer's own precedent rather than case law, so a citator is largely outside its design.
Searched the site, the platform pages, Robin University and the help center entry point on 29 Aug 2026. No material was located addressing whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator license was located. Noted for context: this is a contract review product whose grounding is customer playbooks and contract language rather than case law, so a citator is largely outside its design, though the add in's Research mode does reach legal sources on the web.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
The vendor documents abstention as an explicit design objective. The proprietary model is trained to prioritize faithful extraction from source documents, and where information is not present it is designed to identify that absence rather than invent an answer; the vendor says this is what instills trust for legal professionals. Vendor material separately describes flagging what is absent as well as what is present as a capability advantage.
No published evaluation of the abstention behavior itself was located as of 29 Aug 2026: the ContractIQ Bench results address interpretation accuracy rather than refusal rate.
Searched the site, the platform pages, Robin University, the blog and the help center entry point on 29 Aug 2026. No published material describes what the product does when it cannot ground an answer, and no explicit no answer path was located. Review output carries risk flags at clause level, which ranks how serious a flagged issue is rather than how confident the system is that it found one, and was not treated as a confidence signal for this purpose.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
No court order, opinion or disciplinary record naming this product has been located as of 29 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions worldwide involving AI generated hallucinated content and records the AI tool implicated where it is known. Also checked published 2026 sanctions summaries and secondary sanctions trackers. The entries located name filers, and in some rows other products, rather than this one.
This is a statement about the public record on the date shown and not a clearance. Note that this is a contract lifecycle product rather than a litigation or research tool, so its output is unlikely to reach a court filing as cited authority, and note that the database is weighted toward US filings while this vendor is UK founded and operates across 70 plus countries.
No court order, opinion or disciplinary record naming this product has been located as of 29 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions worldwide involving AI generated hallucinated content and records the AI tool implicated where it is known. Also checked published 2026 sanctions summaries and secondary sanctions trackers. The entries located name filers, and in some rows other products, rather than this one.
This is a statement about the public record on the date shown and not a clearance. Note that this is a contract review product rather than a litigation or research tool, so its output is unlikely to reach a court filing as cited authority, and note that the database is weighted toward US filings while this vendor is UK founded.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
Searched the site, the insights and white papers indexes, the press releases and the resources hub on 29 Aug 2026. No engagement with any named ethics opinion or professional guidance was located, including ABA Formal Opinion 512, US state bar guidance, and Solicitors Regulation Authority or Law Society guidance given the company's UK base. The vendor publishes substantial thought leadership on AI reliability, including a piece arguing that human in the loop alone is insufficient, which engages with the professional risk question in substance while naming no guidance a buyer is bound by.
Searched the site, the blog, the news index, Robin University, the guides and reports index and the webinars page on 29 Aug 2026. No engagement with any named ethics opinion or professional guidance was located, including ABA Formal Opinion 512, US state bar guidance, and Solicitors Regulation Authority or Law Society guidance given the company's London base and UK founding. The vendor publishes substantial educational content about contract workflows and legal AI adoption, which addresses practice efficiency rather than the professional responsibility obligations its buyers are bound by.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Savings are claimed with nothing published on the client's side of the equation. The recurring published claim is negotiation time reduced by up to 90 percent, alongside speed framing throughout including a stated four times faster generation than generalist tools. Searched the site, the platform pages, the customers page and the resources index on 29 Aug 2026 and located no per matter record of AI assisted work intended for fee purposes, and no guidance on billing, fee or client disclosure treatment. The vendor sells to law firms as well as corporate teams, so the firm side of that question applies.
Savings are claimed prominently with nothing published on the client's side of the equation. The headline claim across vendor material is contract review 80 percent faster, with an internal figure of over 85 percent faster from the vendor's own legal team. Searched the site, the platform and services pages, the blog and the guides and reports index on 29 Aug 2026 and located no per matter record of AI assisted work intended for fee purposes, and no guidance on billing, fee or client disclosure treatment. The vendor sells to law firms as well as in house teams, so the firm side of that question applies here.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
Substantial security material is published openly, including a detailed security FAQ, named certifications, a named external security advisory board and a security standards white paper, all reachable without a sales conversation. But no subprocessor list, no statement of which model providers see customer content, and no consent or notification pack for clients that a firm could forward was located as of 29 Aug 2026.
The vendor states that owning its model limits data exposure to additional subprocessors, which is an argument about the shape of the chain rather than a disclosure of it. No list exists to point to.
A trust center is published at a stable URL and can be reached without a sales conversation, which is a real access route, and the security page names Anthropic and AWS as partners. But the documents a client's AI clause asks for were not located as of 29 Aug 2026. There is no subprocessor list, no statement of which model providers see customer content, and no consent or notification material a firm could forward to its own client. A partner logo is not a disclosure of model providers, and no list exists to point to.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Searched the site, the platform product pages, the technology page and the security page on 29 Aug 2026. Vendor material states outputs are traceable and that all data access is tracked and audited, so elements of an access trail exist. But no per document export covering model used, sources retrieved and human verification together was located, and the model used would be difficult to state in any case given the Panel of Judges architecture routes tasks across multiple models.
Noted for context: this is a contracting product rather than a litigation product, so a judicial AI disclosure order is less likely to reach its output.
Searched the site, the platform pages, the security page and Robin University on 29 Aug 2026. Third party sources describe exportable audit logs and reports, which is not vendor material and was not treated as evidence. On the vendor's own property no per document export covering model used, sources retrieved and human verification together was located, and the model used is not identifiable from published material in any case.
Noted for context: this is a contracting product rather than a litigation product, so a judicial AI disclosure order is less likely to reach its output.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- Commercial Transparency
- Third Party Request and Subpoena Notice
- Primary Law Corpus Provenance
- Good Law Verification
- Bar Guidance Alignment
- Outside Counsel Guideline Readiness
- Court Disclosure Support
Which one fits
Choose Luminance if
- Data isolation is the deciding control. Every Luminance customer gets a dedicated single tenant instance with complete isolation and no co mingling, documented alongside a statement that vendor staff cannot view customer documents.
- You want measurement rather than assertion. Luminance publishes ContractIQ Bench, built on 189,000 manually annotated data points across named provision types, tested on held out documents with blind expert evaluation alongside.
- The tool will be used well beyond legal. Luminance publishes dedicated pages for eight business functions and six industries, which is a precise account of who it is built for rather than a broad claim.
Choose Robin AI if
- Word is the only surface your team will accept and the deployment has to be light. Robin AI's Word add in is the primary product, with Ask, Draft, Edit and Research modes separating asking from drafting so the user chooses the operation.
- You want a searchable repository of executed contracts alongside review, rather than a full lifecycle platform you will only partly use.
- You are willing to negotiate the training term yourself. Robin AI's published position is consent based rather than prohibitive, which is weaker as published but is a term a well advised buyer can close in contract.
In summary
Luminance
Luminance is a contract lifecycle platform covering generation, negotiation, analysis, compliance and investigation. It was founded in Cambridge in 2015 by mathematicians and is sold across legal, compliance, procurement, sales, finance, HR and marketing functions. According to the AI Legal Index, its strongest documented control is segregation: each customer receives a dedicated single tenant instance with complete isolation and no co mingling of data, and published access controls state that vendor staff cannot view customer documents. It publishes ContractIQ Bench, a proprietary benchmark built on 189,000 manually annotated data points with blind expert evaluation. Its Panel of Judges architecture has multiple models analyze each clause independently and reach consensus. It publishes no pricing.
Robin AI
Robin AI is a contract review, drafting and negotiation platform for in house legal teams and law firms. It is delivered through a Microsoft Word add in with Ask, Draft, Edit and Research modes, alongside a browser application holding a searchable repository of executed contracts. According to the AI Legal Index, its documented review surface is real: clause level recommendations arrive as tracked changes, comments or highlighted text tied to company standards, which a lawyer accepts or rejects in place. Its published training position is conditional. It states that customer data will not be used for model training or feature development without express consent, which is a consent gated permission rather than a prohibition.
Questions buyers ask
Luminance vs Robin AI: which is stronger?
Luminance, clearly, on published evidence. The separation is concentrated in documentation. Luminance publishes a benchmark, a single tenant isolation model and detailed access controls, while Robin AI publishes real product detail but far less about accuracy, confidentiality and deployment outcomes. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Does Robin AI train on customer data?
Its published position is conditional rather than prohibitive. Robin AI's security page states that customer data will not be used for model training, fine tuning or other feature development without express consent. A consent gate is a permission a customer can be asked to grant rather than a commitment the vendor has accepted. Luminance by contrast documents single tenant isolation with no co mingling. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Which one keeps client data properly separated?
Luminance documents this most precisely. Each customer receives a dedicated single tenant instance with complete isolation and no co mingling of data, which exceeds the segregation level this buyer segment ordinarily requires, and access control documentation states that vendor staff cannot view customer documents. Robin AI publishes real controls but no equivalent segregation statement. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Do either publish accuracy figures?
Luminance does. ContractIQ Bench assesses interpretation of named provision types including liability caps, termination for convenience and confidentiality obligations across 189,000 manually annotated and reviewed data points, tested on held out documents and concepts excluded from training, with blind evaluations by legal experts alongside. Robin AI publishes no accuracy figure, no hallucination rate and no evaluation framework, describing grounding only in outline. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
What do Luminance and Robin AI both leave unpublished?
Neither publishes a rate, a unit of charge or a tier structure, and on both properties every commercial path ends in a demo request. Neither publishes a position on liability or recourse when the AI gets a clause wrong. Neither publishes a position on the advice line or on jurisdiction limits, which is more pointed for Luminance because it sells to procurement, sales, finance, HR and marketing teams alongside legal. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Neither vendor paid for inclusion, placement or a grade, and neither reviewed this page before it published. Everything on it comes from public material on the dates shown. How the index grades.
First, the size of the gap here reflects what each vendor publishes rather than a tested difference in output quality, and Robin AI is a smaller company than Luminance with correspondingly less published documentation. Second, and less forgiving: Robin AI's most specific published performance figure, review completed over 85 percent faster, is attributed to the vendor's own internal legal team rather than a customer. That is the vendor testing itself, not evidence from a deployment. Neither vendor reviewed this page.