Softment

AI by Industry

AI for Legal

Improve document search and reduce manual review work with grounded, citation-ready assistants and validation-first pipelines.

Start smallFixed-scope pilot
Delivery1–2 weeks typical
IncludesSource + handoff
Document search with citations and safer groundingExtraction workflows for structured fields and clausesReview aids with validation and fallbacksAccess-aware patterns and audit-friendly logsEvaluation baselines for accuracy

Industry Problems

Where teams get stuck

Common bottlenecks we see before AI workflows are implemented.

Time-consuming doc review

Contracts and legal docs require careful reading; manual review is slow and hard to scale.

Search limitations

Keyword search struggles with intent and context across long documents and clause variations.

Risk of incorrect answers

Legal workflows need citations, validation, and safer fallbacks to reduce risk.

Access and audit needs

Sensitive documents require access-aware patterns and audit-friendly engineering practices.

AI Solutions

What we build

Implementation-ready modules designed for reliability, safety, and real operations.

Citation-ready doc assistant

RAG with citations and eval sets so answers are grounded and measurable.

Document extraction + validation

Schema-based extraction with validation rules and review hooks for structured outputs.

Hybrid search upgrades

Hybrid retrieval + reranking tuned on eval sets to improve clause and concept discovery.

Guardrails and safety controls

PII-aware patterns, validation, and controlled escalation behaviors for sensitive workflows.

Stack

Suggested implementation stack

A practical stack we can adapt to your constraints and existing systems.

RAG + citationsVector DB (pgvector / Qdrant)Hybrid search + reranking (optional)Schema validation + review queuesEvals + regression checksAccess-aware patterns + audit logs

Automations

Example automations

A few workflows that usually deliver ROI quickly.

Search and Q&A across contract libraries with citations

Extract structured clause fields into review workflows

Improve retrieval quality with reranking and evals

Review queues and approvals for high-risk outputs

Monitoring quality drift and failure patterns

Standard

AI delivery standard

Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.

Logging + tracing

Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.

Guardrails + safety

Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.

Evals + regression tests

Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.

Cost + latency controls

Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.

Documentation + handoff

Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.

Security-first integration

Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.

FAQ

Frequently asked questions

Do you provide legal advice?

No. We build software systems. We design assistants to cite sources and use safer fallbacks, but legal decisions remain with your team.

Can we require citations for every answer?

Yes. We can enforce citation-ready answer patterns and fallback behavior when sources are insufficient.

Can this work with sensitive documents?

Yes. We design access-aware patterns and audit-friendly logs. Exact handling depends on your security and hosting requirements.

How do you measure accuracy?

We build eval sets and regression checks based on real queries so improvements are measurable and predictable.

Ready to start?

Want an AI pilot for your workflow?

Start with a fixed-scope gig or request a tailored implementation plan for your systems.