Legal operations platforms, built to a standard that holds up in court.
We build production-grade software for companies that serve the legal industry: process servers, court reporters, e-filing services, paralegal companies, records retrieval, and other operations where every digital artifact has to survive being entered as evidence or audited by a regulator.
Start with a discovery sprint →What we actually do
Most software for legal-adjacent operations falls apart at one of three places: a data flow that lets the wrong tenant see the wrong matter, an AI feature that quietly modifies the legal record, or an evidence chain that breaks the first time it’s contested. We build systems where none of those failures are possible by design, not by policy.
We focus on a narrow slice of legal operations work where we have shipped production systems and where the architectural decisions matter most:
Multi-tenant SaaS for legal services companies
Platforms where multiple operating companies share infrastructure but cannot, under any circumstances, see each other’s matters, clients, or financials. Tenant isolation enforced at the database layer with PostgreSQL Row Level Security, not application-level filtering.
AI document parsing with human-in-the-loop review
Court documents, intake forms, service requests, contracts, and other unstructured legal inputs converted into structured case data through an AI pipeline that drafts and a human approves. No AI-generated content ever reaches the legal record without explicit human sign-off.
Evidence chains and audit-grade data flows
GPS, timestamps, photos, signatures, and chain of custody captured at the source, timestamped by the database, and stored in a way that makes after-the-fact modification impossible. Audit logs enforced as append-only at the database permission level.
AI-assisted document generation with strict guardrails
Affidavits, non-service narratives, intake summaries, and other legal documents generated from structured case data using templated rendering, not free-form AI output. Where AI is used for language refinement, the raw observation is always preserved alongside the refined version with full audit trail.
If your build doesn’t fit one of these, we will tell you on the first call. We have turned down more legal-tech engagements than we have taken.
How we think about legal operations builds
A few principles that show up in every system we ship.
AI is good for unstructured-to-structured work and bad for deterministic calculation
We use AI to parse messy court documents, extract fields from intake forms, and suggest legally appropriate language. We do not use AI for jurisdiction logic, tax calculation, fee computation, or any other deterministic path where the correct answer is knowable from rules. Code handles deterministic. AI handles ambiguity.
The AI never has the final word
Every AI-generated output passes through a human approval step before it enters the official record. Document parsing surfaces extracted fields for review. Language refinement preserves the raw observation. Generated affidavits require human sign-off. The audit log records both the AI draft and the human-approved version.
Tenant isolation is enforced by the database, not the application
Application-level filtering plus tests plus code review will eventually fail, and the cost of one failure in a legal-tech context is too high. PostgreSQL Row Level Security with policies tied to a tenant context variable makes cross-tenant data access architecturally impossible, not just procedurally prevented.
Evidence is captured at the source and timestamped by the database
GPS coordinates captured at the moment of logging, not retroactively. Timestamps recorded server-side, not from device clocks that can drift or be manipulated. Photos uploaded with metadata intact and immutable after capture. Every piece of evidence has to answer four questions: who created it, when, who changed it, and when.
Audit logs are append-only at the database permission level
A logging table the application can write to is also a logging table the application can be tricked into modifying. We use database permissions to make modification structurally impossible: the application’s database role has INSERT only, no UPDATE, no DELETE, no TRUNCATE.
Field operatives are not legal writers
When server notes, intake observations, or other field-collected data flow into the legal record, they pass through an approval queue first. Raw observations are preserved. Legal language is reviewed by someone accountable for it. This single feature is the highest-value compliance control in any legal operations platform we have built.
Standards, architecture, and controls
The platforms we build are designed against three failure modes specific to legal operations: cross-tenant data exposure, AI-introduced fabrication in the legal record, and after-the-fact modification of evidence.
- Multi-tenant isolation: PostgreSQL Row Level Security enforced at the database layer, not the application
- AI document parsing with human review before any data enters the case record
- Approval queues for any field-captured content before it becomes legal evidence
- Server-side timestamps on all evidence logs; device clocks recorded for reference only
- GPS capture required at the moment of logging; retroactive entry blocked
- Court-grade document generation from HTML/CSS templates with deterministic rendering
- Enforced signing workflows via DocuSign with audit trail on signing order and timing
- Deterministic calculation paths for jurisdiction-aware tax, mileage, and fee logic
- Audit logging on every state change with actor, timestamp, action, and prior value
- Document storage on encrypted S3 with tenant-scoped access control
- Append-only audit logs at the database permission level
Case study
Multi-tenant process serving platform for a Canadian legal services company
Replaced a Gmail-and-spreadsheets operation with a production SaaS handling AI document intake, server dispatch with GPS evidence logging, admin approval queues, court-grade affidavit generation, DocuSign signing workflows, and jurisdiction-aware invoicing. Built so tenants cannot see each other’s data through any application bug, AI cannot modify the legal record without human approval, and every piece of evidence is traceable from source to affidavit.
Read the full case study →How we engage
We start every legal-tech engagement with a paid one-week discovery sprint. You get a written deliverable you own whether or not you continue with us:
- A data flow map of your proposed or existing system, with every legal-sensitive touchpoint identified
- A risk register covering tenant isolation, AI failure modes, evidence integrity, and audit defensibility
- An architecture recommendation with vendor selection, integration approach, and AI scoping
- A phased build plan with milestones and pricing
Most teams find this is the cheapest way to know what they actually need before committing to a full build. It also gives both sides a low-risk way to see if we work well together before signing a larger engagement.
Who we work with
We work with founders and operators running legal-adjacent companies: process servers, court reporters, e-filing services, records retrieval, paralegal services, IP filing services, and other operations where the legal industry is the customer.
We do not currently take on engagements directly inside law firms. The work is meaningfully different from legal operations, the buyer expectations and ethical requirements differ, and we believe in being honest about where our shipped experience is.