AI Agents for Legal
FISTA Solutions builds legal AI agents that handle volume under attorney supervision: first-pass document review, clause extraction into obligation registers, discovery and deposition summarization, matter intake triage, and research memos. Every assertion cites source text, and privilege is protected by architecture.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What can AI agents do in legal teams?
Legal agents perform first-pass review with issue coding, extract clauses and obligations into structured registers, summarize depositions and productions into issue-linked timelines, triage inbound matters with conflicts signals, and draft research memos from approved sources.
- 01
Document review agent
First-pass responsiveness and issue coding with confidence scores, routing uncertain documents to attorney queues.
Review - 02
Clause extraction agent
Builds obligation and clause registers from executed contracts, quoting the contract text behind every entry.
Contracts - 03
Discovery summarization agent
Summarizes depositions and productions into issue-linked timelines with page and line citations.
Litigation - 04
Intake and conflicts agent
Structures inbound matters, surfaces conflicts signals, and routes to the right practice group with rationale recorded.
Intake - 05
Research memo agent
Drafts memos from approved sources and internal precedent, citing every authority for attorney verification.
Research
Requirements
What guardrails do legal teams agents need?
Legal agents handle privileged material and produce work an attorney will sign, so guardrails address confidentiality, supervision, and verifiability: matter-scoped access, no external training on client content, citations on every assertion, and recorded attorney review.
| Guardrail | Why it matters here | How FISTA implements it |
|---|---|---|
| Privilege protection | Privileged content must not cross matters or leave your control. | Matter-scoped retrieval enforced at the data layer, ethical wall rules applied to agent queries, and no model training on client content. |
| Attorney supervision | Professional rules place responsibility on the attorney. | Review gates before any client-facing output, reviewer identity recorded, and no autonomous filing or client communication. |
| Verifiability | Attorneys must check, not trust. | Every assertion quotes source text with a link to the document, page, and where relevant line number. |
| Data residency | Client agreements constrain where content is processed. | Region-pinned processing and model selection constrained by your outside counsel guidelines and client commitments. |
| Defensibility | Review decisions may be challenged. | Full traces per document decision, versioned prompts and models, and exportable decision logs per matter. |
Where AI fits
Which legal teams workflow should you automate first?
Start with clause extraction or first-pass review on a matter with known volume. Both are measurable against attorney work product, both keep the attorney in the loop, and neither requires the agent to communicate with anyone outside the firm.
- 01
1. Pick a bounded corpus
A defined contract set or review population gives a measurable accuracy baseline against attorney coding.
- 02
2. Label a sample
Attorneys code a sample so precision and recall are measured on your material, not on a vendor benchmark.
- 03
3. Tune the threshold
Decide with supervising attorneys what confidence routes to human review, weighted by the cost of a miss.
- 04
4. Run parallel on a live matter
Compare agent coding to attorney coding on real volume before relying on it.
- 05
5. Expand by practice area
Language and issues differ enough across practice areas that each deserves its own evaluation.
Cost and timeline
How much does an AI agent for legal teams cost, and how long does it take?
Cost is driven by corpus size, document management integration, and evaluation depth; timeline by data access and attorney review availability. FISTA does not quote blind: the scoping call returns an agent design, an accuracy plan, and a phased estimate.
Evaluation is the investment that makes legal AI usable. Attorneys will not rely on output they cannot check, so labeled samples, measured precision and recall, and citation discipline are where the budget goes, not on model access.
Integration with your document management system determines reach. An agent that can only see uploaded files helps one matter; one that can search the firm's repository within ethical walls helps the practice. That integration is scoped explicitly.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI agent into production?
FISTA delivers agents in four gated phases: a discovery sprint that picks the workflow and writes the agent specification, a design that names tools, permissions, and approval points, a build with an evaluation harness and shadow runs on real work, and a production release with traces, dashboards, and rollback.
- 1
Select and specify
Choose the workflow with a measurable outcome, map its systems and edge cases, and write the agent spec with success metrics.
OutputAgent specification, golden test set
- 2
Design the guardrails
Tool inventory with least-privilege scopes, approval gates, escalation paths, data handling, and the evaluation plan.
OutputTool and permission matrix
- 3
Build and shadow-run
Implement tools as MCP servers or connectors, iterate against the evaluation harness, and run in shadow mode on live inputs.
OutputShadow-mode results, eval scores
- 4
Release and observe
Graduated rollout, full traces, cost and quality dashboards, on-call runbook, and a change process that re-runs the evals.
OutputProduction agent with SLOs
Why FISTA
Why choose FISTA Solutions to build your legal teams agents?
FISTA builds legal agents where privilege is enforced in the data layer, client content never trains external models, and every assertion is checkable against quoted source text. Work is contracted through a US entity with full IP assignment.
Legal specifics
- Matter scoping and ethical walls are enforced at the query layer, so a walled user cannot reach restricted content through an agent.
- Client content is not used to train external models, and the data flow is documented for client security questionnaires.
- Every assertion quotes its source with a link, so verification is a glance rather than a re-read.
- Review decisions are traced and exportable per matter, which keeps the process defensible if challenged.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What teams ask before deploying agents.
Straightforward guidance for evaluating scope, fit, and the next step.
01Will client data train an AI model?
No. FISTA uses enterprise endpoints with training disabled, or self-hosted models where client commitments require it, and documents the data flow so you can answer client security questionnaires precisely.
02Can an agent review documents without an attorney?
It performs first-pass coding with confidence scores; a supervising attorney reviews uncertain documents and retains responsibility. The record shows what the agent proposed and what the attorney decided.
03How accurate is first-pass review?
Precision and recall are measured against attorney coding on a labeled sample of your material, and the review threshold is set with supervising attorneys based on the cost of a missed document.
04Does this work within ethical walls?
Yes. Walls are modeled as data and enforced at the query layer, so an agent cannot retrieve restricted content for a walled user through search, summaries, or reports.
05How long until it is usable on a live matter?
Typically weeks for a bounded corpus, once documents are accessible and attorneys can label a sample. Expansion to further practice areas follows their own evaluations.
Scoped in writing before you commit
Give the practice throughput without giving up supervision.
Bring the review population or the contract backlog. The scoping call returns an agent design, an accuracy plan, and a phased estimate.