Ajax vs PointOne: how they compare in 2026

Take the models out and there is nothing to sell. The capture layer produces a stream of activity across documents, email, browser, calls, calendar and meetings. Everything the buyer pays for happens after that, when a model reads the content of the work, decides which matter it belongs to, groups fragments together and writes a narrative in the timekeeper's own voice with an activity code. The vendor draws the line itself, contrasting its product with tools that record activity without writing entries. The engine that turns a client's outside counsel guidelines into limits on wording is model work too. Without any of it, what remains is a timesheet of raw activity that a lawyer still has to write up.

Take the models out and there is no product. PointOne's pitch is that a lawyer stops writing time entries altogether: the software captures the working day across documents, email, calls and the web, works out which matter each task belongs to, and writes the narrative, with retroactive capture over any date range for people who reconstruct at week or month end and generated narratives for those who still prefer timers. The same models turn uploaded outside counsel guidelines into structured billing rules, check every entry against them, and mark up draft bills with flagged problems and suggested fixes. What would remain without them is a timer and an export to Aderant or Clio.

For a timekeeping product, accuracy means whether an entry is right: the right work, the right matter, the right duration, and a narrative that describes what happened. The method is described. The vendor says it reads the content of the work rather than window titles or calendar subjects, clusters activity scattered across the day on one matter into a single entry, and identifies the client and matter by matching what is on screen against a live map of the firm's matters. It also publishes a figure, twice in an explainer of 7 July 2026: matter matching is right about 92 percent of the time and improves as the system learns a firm's people, documents and topics. Publishing a figure below 100 is candid, but it is not something a reader can test. There is no sample, period, method or definition of a correct match, and nothing says what happens to the rest beyond the lawyer's review catching them. No figure is published for the narratives themselves, as distinct from matter matching.

Accuracy is the whole claim, and none of it is measured. The product writes billing narratives from captured activity rather than legal assertions, so the question is whether an entry describes what the lawyer actually did and lands on the right matter. Customers on the homepage say it is accurate. The April 2026 release says the office stress tested whether PointOne could correctly identify a wide range of billable and nonbillable tasks, and the security page promises auditability of AI outputs. No accuracy rate, sample, benchmark or error rate is published anywhere. Nothing describes what an entry is built from, or how a reviewer can tell an entry attributed to the wrong matter from a correct one before it reaches a client's bill.

The review step is built into the product, not just advised. Nothing the model writes reaches the firm's billing system on its own. Entries are drafted through the day, only the timekeeper can see them until they release them, and release is a deliberate act. The timekeeper can edit or delete any entry and stop capture with one click, and the vendor says nothing is captured until they choose to resume. The FAQ also answers supervision the other way: the firm cannot see a timekeeper's hours before release. The rest of the control structure is not published: no point at which the model declines to write an entry, nothing on what happens after a wrong entry goes out, and no statement of what the system may not be used for.

The system drafts and people approve, and the approval path is published in detail. Time entries are generated automatically and then move through Review, where the AI marks up the draft bill and a firm sets its own reviewers, routing and permissions. The worked example runs a bill from legal assistant to responsible partner to billing administrator, with comments and tagging to resolve queries. Billing rules drawn from client guidelines are enforced against every entry before it gets that far. No limit the system places on itself is published. There is no threshold at which it declines to classify a task, no statement of what an unreviewed entry may not be used for, and nothing on what happens when capture misreads the matter.

Deployment evidence is extensive, but undated and without method. Ten firms are named with practice area, city and a figure. They include Almazan Law in Florida at 1.1 extra billable hours a day, Poole Huffman in Georgia at $32,000 recovered a month, Kirker Davis in Austin at $4,639 per timekeeper a month, Pines Federal at $42,000 a month, King Law at 121 active users across 24 offices, and Quill & Arrow at 270 professionals. Named attorneys are quoted for each. Sixteen more testimonials carry names, titles and firms, and the vendor says fourteen customers went on to invest personally. But no case study carries a date, and no method is given for the return multiples, so figures like 67x and 58x cannot be checked or compared.

One deployment is named, dated and sized, and no result is measured. PointOne's April 2026 release states that the Minnesota Attorney General's Office rolled the platform out across its entire office of more than 300 timekeepers. That followed a pilot that tested billing across several agencies, dynamic review flows and classification of billable and nonbillable tasks, and the release quotes Assistant Attorney General Eric J. Kolbeck by name. The homepage carries logos and attributed quotes from firms including O'Hagan Meyer, Scarinci Hollenbeck, Bell Nunnally, McCathern, Lydecker, Elevare Law and Bevilacqua PLLC, with partners, a CFO and a COO speaking on the record. No published material gives a figure: no measured capture uplift, time saved or realization change. The only quantities are customer counts and the size of one office.

For a product that reads everything on a lawyer's screen, the published commitments are substantial, with one structural gap. The addendum of 5 August 2026 prohibits training, retraining or fine tuning on customer data and binds the named model providers to the same. Raw data is deleted after 30 days and processed data after 60, and everything is deleted within 30 days of termination. Confidentiality runs both ways in the terms. Access is handled at the level this product needs: drafted entries are visible only to the timekeeper until released, and capture stops with one click, which is the control a lawyer on a sensitive matter needs. The vendor says in its own words that its users handle privileged client data. But privilege and work product are not addressed as such, and nothing covers separation by matter, which matters when a timekeeper is screened off a matter the capture would otherwise read.

The commitments are specific, and they are published on a security page rather than in a contract. That page states full data segregation, zero training on firm data, zero data retention by the AI models, customizable data retention and single tenant options for enterprise deployments. It also states customizable geolocation, the ability to run models inside a private Azure cloud, and compatibility with systems on premises. The Privacy Policy dated 7 April 2026 says in terms that it does not apply to Customer Data, which is governed by contracts with customers that are not published, and the published Terms of Service cover the website only. Nothing addresses privilege or work product in what is captured. PointOne watches a lawyer's documents, email and calls all day, so what it holds is a map of client matters.

A real position on review before billing is published, and it lives in the wrong place to earn more. An explainer of 7 July 2026 states that the lawyer's job shrinks to review, check the drafts, fix what's wrong, release to billing; that this does not mean the software bills for you; and, answering whether the output is accurate enough to bill from, that the entries are drafts until a lawyer approves them, which is what keeps billing defensible. The same page answers whether lawyers still review entries before they are billed with a plain yes, and says drafts stay drafts until the lawyer approves them. That engages the duty this product actually raises, which is the lawyer's responsibility for a narrative the client will read as their own account of their work. Three things hold it here. The position sits in a category explainer on the blog, not in the terms, the product pages or anything a customer signs. The fee rules the vendor names elsewhere are never connected to its own product. And nothing addresses found time, flat fees, or what a client is told about how the entry was written.

A position on the professional duty is published, on the vendor's blog rather than in anything a customer signs. A guide of 15 July 2026 states that no AI time tracking software should submit entries without human review. It says the firm reviews, edits if necessary and approves every entry before it goes anywhere, and that this is both an ethical requirement and a practical quality control measure. The same page says nothing goes to billing without the timekeeper's approval. That engages the duty this product raises: the lawyer bills for time actually spent, signs the bill and stays answerable for every entry, while the words describing the work are drafted by software. The position is not in the Terms of Service of 7 April 2026 or on the product pages. No rule of professional conduct or ethics opinion is named on the site. Nothing addresses found time, flat fees, or what a client is told about how the narrative on the bill was written.

Nothing is published about how the models are governed. No one is named as accountable for what they write, nothing describes testing before a change ships, no evaluation results appear, and the privacy policy says plainly that the vendor does not test, verify or endorse AI outputs. Nothing addresses whether entry quality holds up across practice areas, seniority, or work that leaves little digital trace, which is the obvious question for a product whose output becomes a bill. The closest thing to governance is the outside counsel guideline feature, which shows extracted rules to the firm with a link to the source page before they take effect, but that governs the client's rules, not the model.

The security page has a section headed Responsible use of AI, and everything in it is a data control: zero training on firm data, zero retention by the models, customizable base models, private Azure execution and auditability of AI outputs. Those protect the firm's data; they say nothing about how PointOne builds and tests the models that classify a lawyer's day. No accountable owner is named, nothing describes what is tested before a release ships, no evaluation result is published, and nothing addresses whether classification or narrative quality varies by practice area, seniority, working pattern or language.

All five elements are published in specific terms. Retention: raw data is deleted after 30 days and processed data after 60, stated the same way in the terms, the privacy policy and the addendum, and changeable only by written customer instruction. Deletion: everything is deleted or irreversibly anonymized within 30 days of termination. Access: SAML 2.0 and SCIM provisioning through the firm's identity provider, central control, automated onboarding and offboarding, and entries visible only to their author until release. Subprocessors: seven are named, including the model providers. Incidents: notice to the customer within 48 hours of becoming aware, with a commitment to remediate. Encryption is AES-256 at rest and TLS 1.2 or better in transit. One inconsistency: the security FAQ says the vendor does not store customer data, while the agreement and addendum publish the 30 and 60 day windows.

The security page publishes customizable data retention, full data segregation, zero data retention by the AI models and single tenant options. The Privacy Policy dated 7 April 2026 covers the capture surfaces. Data taken through the Zoom integration, including meeting attendance, participant names and call logs, is discarded immediately once time entries are generated. Data accessed through Google Workspace APIs is not used to develop or train AI or machine learning models, is retained only as long as needed, and is deleted on request through an automated process. There is no subprocessor list beyond analytics providers, no incident or breach notification commitment for firm data, and no published agreement, since the Privacy Policy excludes Customer Data.

What is published disclaims the exposure rather than allocating it. The service is provided as is, with no warranty of any kind and no undertaking that it will be error-free; AI outputs are expressly not guaranteed to be accurate, complete or suitable for any purpose, and the privacy policy adds that the vendor does not test or verify them. Liability is capped at the fees paid in the preceding twelve months and indirect, incidental, special, consequential and punitive damages are excluded. No indemnity of any kind runs from the vendor to the customer, not even for intellectual property, and no insurance is mentioned. For a product whose output is a bill a client will scrutinize and a lawyer will sign, nothing published says what happens if an entry is wrong.

The only published allocation of loss is a short disclaimer in website terms. Section 5 of the Terms of Service dated 7 April 2026 says that in the event of data loss or business interruption resulting from use of the services PointOne is not liable for damages, and that users are responsible for safeguarding their own data; section 11 caps total liability at one hundred dollars and excludes consequential damages, and section 10 runs an indemnity from the user to PointOne. Those Terms govern the website. No warranty, indemnity or insurance position covering the product is published, and nothing addresses who bears the loss if a wrong narrative reaches a client's bill or a client's billing guidelines are breached by an automated entry.

The integrations are real and named, and the depth stops at a sentence each. Eight billing and practice management systems are supported by name, each with its own page: Clio, MyCase, Filevine, PracticePanther, SurePoint, Centerbase, Actionstep and Soluno, with the claim that most other billing systems work too. What moves is clear enough: released time entries with matter, activity code and narrative already applied, out of Ajax and into the system of record. An explainer of 7 July 2026 goes further and calls two-way sync with all eight table stakes, without saying what comes back the other way, or what happens to an entry edited in the billing system after release. On the capture side the product connects to work email, Microsoft Teams or Slack, calendar, Zoom and supported VoIP platforms, with RingCentral, Dialpad and VXT named. What an implementer would need next is not published: no field mapping, no sync behavior on edits or deletions, no error handling, and no API documentation, which the vendor offers to share with a technical team on request.

The connections are named on both sides of the product and partly described. PointOne captures context from the applications a lawyer works in, including documents, email, calls and the web. The Privacy Policy sets out what two of those carry: the Zoom integration collects meeting attendance, participant names and email addresses and phone call logs, and Google Workspace data is accessed under stated restrictions. On the output side it syncs with billing and practice management systems named on the homepage: Aderant, Elite 3E, Clio, Filevine, Actionstep, Surepoint, MyCase, LeanLaw and QuickBooks. The security page says it works with systems on premises. There is no integrations page, documentation or API reference, and nothing on what syncs in which direction or what a firm must configure.

The product is clearly delivered from the cloud, and neither tenancy nor residency is answered. There is a desktop app and a hosted service behind it. Nothing published names a hosting provider, region, data center or tenancy model, or separates where the software runs from where captured work sits. The addendum comes closest. It says customer data may be processed in the United States and other countries where the vendor operates, naming no other country and offering no choice, and commits to standard contractual clauses for European transfers and Australian privacy law for Australian ones. For a product that reads privileged documents across a firm's whole working day, a security reviewer has nothing published to start from.

The tenancy model is stated plainly, and the geography is offered without being specified. The security page lists flexible deployment options for large firms: full data segregation, single tenant options for enterprise deployments, customizable geolocation, compatibility with systems on premises, and the ability to run models within a private Azure cloud. That is the clearest statement found of where the AI processing can be placed. No region is named, and nothing says which plan or contract carries single tenancy or a private model environment. The Privacy Policy says personal information may be transferred, processed and stored anywhere in the world.

The certification is stated plainly, but the evidence behind it cannot be reached. The security page claims SOC 2 Type II certification, describing independently audited security controls, data handling and infrastructure, and the terms add CASA certification. Independent audits, penetration testing and round the clock endpoint monitoring are also claimed. None of it has detail behind it: no auditor is named, no report period, scope or date is published, and there is no trust portal or route to the report. The only offer is a security review call, booked through the same form as the sales demo.

The certifications are named, and the evidence cannot be reached. The security page states SOC 2 Type 2 certification with independently audited controls, full ISO 27001 certification, GDPR compliance by design, and that PointOne meets HIPAA requirements for firms in practices close to healthcare. No auditor, scope statement, report period or date appears. There is no trust portal or route to request a report, and no penetration test is mentioned. Neither the SOC 2 report nor the scope of the ISO certificate can be obtained from the published site.

The providers are named, and the disclosure stops there. The addendum lists seven subprocessors and three of them are model providers: OpenAI, Anthropic and Google Gemini, alongside BaseTen for model serving, Amazon Web Services, Vercel and PostHog. The same section binds all of them not to retain or use customer data for their own training or fine tuning, which is the part a firm's client would ask about. What is absent is everything downstream of the name: no model or version is identified, nothing states which provider handles which part of the work, nothing says where inference runs, and no commitment is given to notify customers when any of it changes.

The security page offers customizable base models and the ability to run models within a private Azure cloud, and commits to zero data retention by the AI models, without naming a model, version or provider. Nothing states where inference runs by default as opposed to in the private option, and no commitment is published to notify customers when the models behind their time entries change.

The shape of the deal is published in detail and the number is not. There is no pricing page; the vendor answers the question in a post of its own titled how much Ajax costs, and answers it structurally: priced per seat rather than firmwide, because a firm should not pay for timekeepers who keep their own system, with a stated expectation of 70 to 80 percent take-up; contracts are annual; a two-week pilot on the firm's real work comes before any commitment. Elsewhere the vendor frames cost against return, saying one recovered hour per user per month covers the subscription and that payback typically lands inside eleven days, and describes itself as premium-priced in its category. The exact figure depends on firm size and seat count and is quoted on a call. No list price, no tiers and no range appear anywhere on the estate.

No price, rate or unit of charge is published. There is no pricing page, no plan, no rate per timekeeper and no trial, and every route ends at a demo request. One customer quote says the software more than pays for itself, which is the closest the site comes to a commercial statement.

Coverage is shown through customers rather than claimed. The named firms span workers' compensation and insurance defense, general practice, family law, personal injury and business litigation, firms with several practice areas, lemon law, community association work and federal employment law. They are in Florida, Colorado, Virginia, Texas, the Carolinas, Tennessee, California, Georgia, Maryland and the District of Columbia, and range from solo and boutique practices to 121 users across 24 offices and 270 professionals. The real limit is implied, not stated: the product is built for timekeepers who bill by the hour and work on a screen, so contingency work, flat fee work and work done away from a computer fall outside it. Nothing published says so, or names practice areas or firm sizes the vendor does not serve.

The range of buyer is evidenced rather than asserted, and the edges are not drawn. Named customers run from small firms and boutiques (Elevare Law, Bevilacqua PLLC, North City Law, Legal Scale) to larger national firms (O'Hagan Meyer, Scarinci Hollenbeck, McAngus Goudelock & Courie, Bell Nunnally, Lydecker) and a state Attorney General's office with more than 300 timekeepers, and the product pages say the review workflow is flexible enough for firms of all sizes while the security page addresses the needs of the world's largest firms. Worked examples span trademark prosecution, insurance defense, patent litigation and M&A. Nothing states which practice types or billing arrangements the capture handles poorly, and the customer count differs between pages: the homepage says more than 200 firms, the April 2026 release says more than 100.