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Aloi
Aloi is a decision intelligence product for law firms from Aloi AI AB in Stockholm, founded by former Roschier partner Johan Häger. It ingests a firm's own archive of signed agreements, negotiation history, approvals and advice and makes it usable in drafting and negotiation. A precedent finder ranks comparable agreements across the firm's document system by structure, clauses, deal terms and context, with the evidence behind each result.
Autocomplete turns agreed terms into a full long form agreement, placing clauses and definitions and flagging conflicts for review. Aloi also reads prior agreements to show market practice, recurring risks and negotiation patterns, suggests alternative wording in the firm's style, and tests responses to a counterparty's redline against the firm's earlier positions. Atlas, launched in September 2026, extends this to negotiation guidance.
The software runs inside the firm's own cloud or chosen infrastructure on Microsoft Azure, inheriting its permission model. Roschier, Avance and BAHR are quoted as customers. Prices are not published.
Alternatives to Aloi
Vendors in the same categories as Aloi whose graded profiles look most alike, with a line on what each product does.
Similar is not the same, so check that each one does the job you need. No vendor pays for placement.
- TThoughtRiverThoughtRiver is an AI contract review platform for corporate legal teams and the law firms that serve them, from ThoughtRiver Ltd of Cambridge, England.
- LLuminanceLuminance is a contract lifecycle platform covering generation, negotiation, analysis, compliance and investigation for corporate legal teams and law firms.
- AAracor AIAracor is a deal platform that reads the documents behind a transaction and ties every finding to the language it came from.
- MMarveriMarveri is an AI due diligence platform for transactional legal work.
- CCentariCentari is a deal intelligence platform for transactional law firms and investment funds.
- DDraftwiseDraftwise is AI for drafting, reviewing and negotiating contracts inside Microsoft Word, working from a firm's own precedent rather than a generic corpus.
Ordered by how closely the graded profiles match across the 15 capability axes, inside the same categories. See how each axis is graded in the methodology.
Capability grades
All 15 axes, graded from public sources on the date shown. 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.
Every function is model work over the firm's own archive. Aloi ingests signed agreements, redlines, approvals and advice continuously and structures them for retrieval. The precedent finder ranks comparable agreements by structure, clauses, deal terms and context. Autocomplete places clauses, definitions and terms from an agreed term sheet into a long form agreement and flags conflicts and inconsistencies. Judgment analysis surfaces market practice, recurring risks and negotiation patterns across similar matters, and redline review weighs a counterparty's edits against the firm's earlier outcomes and proposes counterproposals.
Atlas, shown in September 2026, adds scenario assessment and negotiation guidance drawn from the same history. Without the models there is no product to sell.
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.
Results show the evidence behind them: each precedent and each piece of guidance points back to the firm's own agreements and decisions, and Aloi calls itself evidence first. Retrieval runs over the firm's documents rather than primary law, so citations lead to internal precedents. Retrieval precision is named as a focus, but no accuracy figure, test set or failure analysis is published, and how a suggestion is grounded when no comparable precedent exists is not described.
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.
The product proposes and the lawyer decides. Every change from Autocomplete is reviewable before it reaches the final agreement, drafting suggestions stay transparent and editable, and redline review offers counterproposals for the lawyer to test rather than sending them. The company states that AI should enhance professional judgment and never replace responsibility, and the system keeps a full audit log. Guardrails can be set to the firm's legal standards and risk posture. What runs without review and what happens after a wrong suggestion are not described.
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.
Three Nordic firms are quoted by name. Niklas Eriksson, partner and head of finance and restructuring at Roschier, describes turning the firm's proprietary knowledge into intelligence for daily work. Eeva Riivari, a partner at Avance, describes putting precedents, positions and negotiation history at lawyers' fingertips, and Joachim Edin, head of innovation at BAHR, describes the firm's data reaching lawyers' workflows.
More than 200 legal industry leaders attended the company's Legal Intelligence Forum in Stockholm in September 2026, where Atlas was first shown. No figures for time saved or outcomes are published.
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.
The software runs inside the firm's own cloud or designated infrastructure and does not take custody of the data, which stays within the firm's VPC under its own policies. It inherits and enforces the firm's permission model, staff have no access by default, and any support access is authorized, limited in scope and logged. The firm decides what is ingested, who can access it and how long it is kept. Firm data never trains anyone else's models.
These are website commitments; no customer agreement is published, privilege and work product are not addressed by name, and the position of the model provider is not stated.
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.
Buyers are law firms and in house teams working on complex agreements. The company's stated view is that AI should enhance professional judgment and never replace responsibility, with decisions documented in a defensible, governed way. No terms of service are published, so no disclaimer on legal advice, user eligibility or jurisdiction limits can be read.
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.
Governance is described as built in: evidence first, permission aware and fully auditable, with guardrails that keep output within the firm's legal standards and risk posture. Retrieval is scoped to limit unnecessary data exposure. No AI policy, release testing, accountable owner or analysis of uneven output is published.
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.
Data is encrypted with AES 256 at rest and TLS 1.2 or higher in transit, backups are encrypted, and tenants are logically isolated. Access runs on role based controls, SSO through SAML or OIDC, multifactor authentication and full audit logging, and logging can feed the firm's own monitoring. The firm sets retention and deletion policies and can export or delete data. Data processing agreements are available and subprocessor disclosure is promised, but no subprocessor list or incident notice practice is published.
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.
No terms of service, customer agreement or limitation of liability is published. The website carries a privacy policy and a cookie policy only, so caps, indemnities, warranties and recourse for wrong output cannot be read before signing.
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.
Precedents are retrieved from across the firm's document management system, and the drafting and negotiation tools work inside the existing drafting workflow, with logging that feeds the firm's own monitoring. Ingestion runs continuously rather than by manual upload. The document systems and drafting tools it connects to are not named, and what syncs, in which direction and what the firm configures are not described.
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.
Deployment is inside the customer's own cloud account or designated hosting environment, built on enterprise infrastructure such as Microsoft Azure. Data stays within the firm's VPC, network boundaries are set by the firm's security team, and the firm chooses the region. Where model processing happens, and whether any request leaves the firm's environment, is not stated.
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.
Under the heading of independent validation, Aloi lists ISO 27001, regular third party penetration testing and secure development practices, and says it aligns with recognized standards. Whether ISO 27001 is a certification, and its scope, date and certification body, are not stated. Detailed security documentation is available on request, and there is no trust center.
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 models behind Aloi, their providers and where they run are not named. Aloi's own commentary notes that most legal AI platforms are built on the same GPT or Claude models and argues that the advantage lies in the firm's data, without saying which it uses.
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.
Pricing is not published in any form: no figure, plan, unit of charge or contract term appears on the site. Every route leads to a demo request.
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.
Law firms and in house legal teams working on complex agreements are the stated audience, with drafting and negotiation of long form agreements at the center and its first customers among Nordic transactional firms. The site runs in English, German, Spanish and Swedish. Practice areas, firm sizes and jurisdictions covered or excluded are not set out.
4 public documents
The public pages on file for Aloi, with the recorded signals each one supports and the date it was last read. Open any of them and check the reading against the record.
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aloi.law/security5 signals
Client Data in Training, Prompt and Output Retention, Ethical Walls and Matter Segregation and 2 more
Read Oct 13, 2026
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aloi.law/legal/privacy-policy1 signal
Third Party Request and Subpoena Notice
Read Oct 13, 2026
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aloi.law/product1 signal
Billing and Fee Posture
Read Oct 13, 2026
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Fabricated Citation Record
Read Oct 13, 2026
Legal Signals
What each signal meansA signal records what public sources say on the date shown. It is not a grade and it is not a recommendation. Where a signal reads Not addressed, it means the index did not locate the material in public sources on that date, which is a statement about disclosure rather than about the product.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
A public policy or trust page states no training on customer content, with no matching term located in the published agreement.
Firm data is never used to train anyone else's models, and none of it leaves the firm's own infrastructure under Aloi's deployment model. The commitment is worded around other parties' models and appears on the security page. No customer agreement is published.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
The customer controls the retention window, by product configuration or by contractual instruction, but zero retention is not stated as available.
The firm sets retention and deletion policies for the data Aloi holds, and storage stays in the firm's own cloud account where applicable. Export and deletion are supported. No default period or zero retention setting is described.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Retrieval enforces the source system access model at query time, per user, and the vendor documents it.
Retrieval applies the firm's existing access rules, and the firm decides who can reach each body of content. Retrieval is scoped to limit unnecessary exposure, and vendor staff have no access by default. How permissions are read from a specific document system is not described.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Published terms or policy address disclosure to authorities or in response to legal process, and no commitment or reservation regarding customer notice is located anywhere. The vendor has told the customer that data can leave and has said nothing about whether the customer hears of it.
The privacy policy, last updated on 30 March 2026, allows disclosure of personal data to comply with laws, legal processes or government requests, with no commitment to notify the customer. No transparency report is published. Customer content held in the firm's own infrastructure is not covered by a published agreement.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
No located public material identifies the corpus behind the product’s answers.
The product works on the firm's own agreements, negotiation history and advice rather than on a collection of primary law. No statute or case law source is named.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
No located public material addresses whether authority is checked for subsequent history.
Results come from the firm's own precedents and negotiation history rather than primary law, so no check of whether an authority remains good law is described.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
No located public material addresses what the product does when it cannot ground an answer.
Each result shows the evidence behind it, and guardrails keep output within the firm's standards. What the product does when no comparable precedent exists, whether it declines or flags low confidence, is not described.
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 addressing fabricated or hallucinated legal citations produced by this product has been located as of the date shown. This is a statement about the public record on that one subject, not a finding about the product, and this signal is not a litigation history.
The AI Hallucination Cases database maintained by Damien Charlotin, which records court decisions worldwide that address hallucinated AI content and the tool involved where known, has no entry naming Aloi. Aloi drafts and negotiates agreements from a firm's own precedents rather than citing case law in court filings.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
No located public material engages with bar or ethics guidance.
The company's position is that AI should enhance professional judgment and never replace responsibility. No bar guidance, ethics opinion or professional conduct rule is discussed.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Public materials claim time savings without addressing billing or disclosure, and the product sits inside a fee relationship between a lawyer and a client where those savings would change the bill.
Law firms buy the product on a promise of full agreements from agreed terms in minutes and faster, better informed decisions. Every suggestion and change is logged. No guidance on billing clients for AI assisted drafting or disclosing it is published.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
The material exists behind a sales conversation or an executed agreement.
Data processing agreements are available, subprocessor disclosure is promised, and detailed security documentation is sent on request. No subprocessor or model provider list is published.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Some elements of the record are available, short of a document level export.
A full audit log is kept, the evidence behind each precedent and suggestion is shown, and Autocomplete changes are reviewable before they enter the agreement. No export that ties a document to the model, sources and reviewer is described, and the product drafts agreements rather than court filings.