For law firms (SRA-regulated)
- Deposition / witness-statement summariser
- Discovery & disclosure triage
- Case law précis — ratio, dicta, relevance
- Retainer-letter drafter
- Conflict-check assistant
- Bundle indexer ("what’s in these 400 pages")
VantagePoint Networks — VP Lab
For London law firms, accountancy practices and financial advisers who can’t put client data in ChatGPT.
Twelve interactive AI tools. Public demos run on Groq; the private deployment runs on your own hardware.
Twelve tools · one platform
Upload up to three files, ask questions, get answers cited back to the source.
Try it →Summary, risks, action items and follow-up questions from any text.
Try it →GDPR-style PII redaction with a labelled entity list.
Try it →Raw notes in, structured owners/priorities/decisions/open questions out.
Try it →Clause-by-clause flags for auto-renewals, liability, IP and more.
Try it →Vendor, line items, VAT, totals — with one-click CSV export.
Try it →Turn legal, financial or technical jargon into everyday language.
Try it →Paste a client email — get summary, actions, questions and a drafted reply.
Try it →Paste two versions of a contract or policy — see exactly what changed and whether it matters.
Try it →Paste a supplier contract, NDA or T&Cs you’re asked to sign — get a sign/negotiate/walk-away verdict.
Try it →Paste an inbound complaint — get severity, regulatory flag, SLA clock and a first-draft acknowledgement.
Try it →Upload a board pack — get a three-line brief, RAG flags and the questions the board should ask.
Try it →Why private AI, not ChatGPT
Demos like the twelve above show what’s possible. A private deployment is why companies actually pay for this — and it solves six problems a public API can’t.
Client files, contracts, payroll, meeting recordings — none of it leaves your network. No third-party API, no logs you don’t control, no "we may use your data to improve the service" small print.
Public AI APIs charge per token. 50 staff using a document assistant 10 times a day adds up to thousands per month. A private deployment is hardware + power — cost is flat no matter how heavily your team uses it.
API outages, ISP blips, a cable cut in a data centre three counties away — public AI goes down with it. Local inference keeps running whether or not the outside world is reachable.
Your policies, your terminology, your historical contracts, your product docs. A model that’s seen your actual operation gives dramatically sharper answers than a generic one that hasn’t.
GDPR, FCA, SRA, ISO 27001 — regulators want clear data flows. "It went to an American cloud AI provider" is a harder story than "it never left the building." Your logs, your retention, your subject-access response.
Vendor changes terms? Raises prices 3×? Rate-limits you at month-end? Swap the model, swap the provider, keep the same hardware and workflow. You own the platform, not the subscription.
The twelve demos above run on Groq so anyone can try them. A private deploymentruns the same class of model on hardware you own, inside your network — book a free 20-min call to scope one →
The honest bit
These public demos send your text to Groq(a third-party AI API) for processing — so please don’t upload real client data here. The private deployment runs the same class of model entirely on your own hardware: zero data leaves your network, no third-party API in the loop.