AI-powered legal solutions
We build contract intelligence, legal research copilots, compliance automation, eDiscovery and matter management platforms. The difference is what sits underneath: grounded citations, matter-level permissions, and an audit trail your buyer's risk team can inspect line by line.
45 minutes with a solution architect. Four fields.
Not ready to talk? Send us your product spec and we will reply with a feasibility read.
Get a Free Feasibility ReviewDelivered for
Asset needed: replace text wordmarks with approved client logo files; confirm usage permission per client.
Pick the description that fits. We will take you to the form with your build type already selected.
You have design partners, a wedge use case, and pressure to ship before the next round. You need an engineering team that can take a spec to a demo-able product without you writing the architecture yourself. We take you from MVP to enterprise-ready.
Build My LegalTech MVP Legal ops & GCYour team is buried in contract review, intake triage, and obligation tracking. Off-the-shelf tools do not fit your workflow, and your security team will interrogate anything that touches privileged material. We build the internal platform your risk team will approve.
Automate My Legal Workflow Firms & ALSPsResearch, document review, and drafting eat billable hours. You want AI that speeds up associates without putting a single unverified citation in front of a client. We build with a human review gate on every output.
Build a Safer Legal AIFor teams where contract turnaround is the bottleneck.
For firms where research hours are the cost centre.
For regulated businesses tracking obligations across jurisdictions.
For high-volume document work that still runs on manual review.
For litigation and diligence at volume.
For legal ops teams running on spreadsheets and email.
Not sure which of these your problem is? Book the scoping call and we will tell you straight Let's talk
Your buyer's real question is not "can you code." It is whether this survives contact with a lawyer, a regulator, and a security questionnaire.
01
Every output is bound to the retrieved passage it came from, at clause or paragraph level. When the evidence is thin, the system says so instead of guessing. A legal user who cannot verify an answer in one click will stop using the product within a week.
02
Access control is attached at ingestion, so a user can only retrieve what they were already entitled to read. Tenant isolation, matter-level walls, data residency options, and no training on client data.
03
Prompt, retrieved sources, model version, output and approving reviewer, all logged immutably. This is what turns a useful tool into something admissible in an internal audit, an SOC 2 review, or a dispute.
04
Word, Outlook, Teams, SharePoint and DocuSign integrations, plus licensed research sources. We have shipped legal builds against LexisNexis, Microsoft 365 and Word directly. Other DMS platforms including iManage and NetDocuments are supported through their APIs.
05
Model routing, caching and small extraction models for the high-volume work, so cost per matter does not scale linearly with usage. We size this during discovery rather than after your first invoice from a model provider.
Ask us how we would handle any one of these for your product. Book the call
Model output
The counterparty may terminate for convenience on , but the exclusivity covenant survives termination for . This deviates from your playbook cap of 12 months, so the clause is flagged for .
On the indemnity cap, the system abstained. Retrieved evidence was insufficient, so no figure was generated and the question was routed to a reviewer.
Retrieved sources
Real engagements and real metrics. Client names are anonymised where permission is not on file.
A legal-services firm
Lawyers were spending hours researching case law, drafting documents and preparing litigation strategy.
We built a research and drafting assistant on GPT-4-class models, LangChain, vector databases, FastAPI and React, with LexisNexis and Word integration, that surfaces precedents, summarises judgments and prepares first-draft briefs under attorney review.
An enterprise organisation, contract operations
Contract creation, approvals, renewals and compliance were fragmented across business units.
We built a multi-agent platform on the OpenAI Agents SDK, LangGraph, FastAPI and React, integrated with DocuSign, SharePoint and Microsoft 365, where specialised agents generate contracts, compare revisions, route approvals, monitor renewals and flag compliance risk.
A RegTech firm
Tracking regulatory change across jurisdictions and staying audit-ready was a manual effort.
We built a platform on GPT-4-class models, LangChain and document intelligence that monitors publications, summarises changes, maps obligations to controls, finds gaps and prepares audit-ready reports.
A financial-advisory firm
Diligence teams spent weeks reviewing financial, legal, operational and compliance documents.
We built an agent platform on the OpenAI Agents SDK, LangGraph and Azure Document Intelligence that analyses contracts and records at volume, identifies risk and generates review-ready reports.
Also delivered in the legal and governance space
Want the full technical write-up on the contract lifecycle build? We will send it after the call. Book a strategy session now
Five stages, plus the governance layer beneath them. Stages two to four are where most legal AI pilots fail.
Solution Architect, AI & Blockchain, SoluLab
Imtiyaz designs the technical architecture behind AI systems for enterprise and startup clients at SoluLab. With 6+ years across complex technical domains, he specialises in translating business requirements into system architecture, evaluating technology choices, and making sure designs hold up against security, scalability and compliance requirements before implementation begins.
“Good architecture is the difference between a demo and something that survives real production load.”
SoluLab was co-founded by Chintan Thakkar, previously a Vice President at Goldman Sachs building software in regulated financial environments, and Rajat Lala, previously a principal software architect at Citrix.
Four stages, each with its own entry point. You can stop after any of them.
1Scoping call
Your use case, data reality, integration constraints and compliance obligations. You leave with a build approach and an honest feasibility read.
Free, this week
2Discovery sprint
Workflow mapping, architecture, stack decisions, clickable prototype and a fixed-scope build plan with costs.
2 weeks
3Production pilot
In front of real users. Grounded citations, audit logging and access controls included from the start, not phase two.
2–4 weeks
4Scale
Product, AI engineering, QA and DevOps. Additional workflows, enterprise integrations, security review support.
Ongoing
Verify: confirm this matches SoluLab's standard contract position before launch.
Start at step one with experts! Call now
“I care less about what AI can demo and more about what it survives in production.”
Models
Retrieval
Agents
Document intelligence
Backend
Frontend
Cloud
Integrations
Governance
Bring us a spec, a half-formed idea, or a pilot that stalled. In 45 minutes you will know whether it is buildable, roughly what it takes, and what we would do differently. If we are not the right partner, we will say so on the call.
Four fields. Nothing about budget.
Form recommendation — a change from the source brief. The brief specified seven fields including phone and budget range. On cold paid traffic a budget dropdown reliably suppresses form starts, because a founder who has not fixed a budget will abandon rather than guess. Capture four fields, then use progressive profiling: ask for phone, budget and timeline once the lead is on the calendar.
Free 45-min
scoping call
