We build legal AI that survives contact with real client data
Legal AI demos look impressive until real documents arrive. Scanned PDFs with OCR errors. Contracts assembled from three templates over a decade of amendments. Clauses negotiated across 47 emails using terminology specific to maritime insurance in Singapore. We build legal AI that handles this reality.
You're here because your legal AI isn't production-ready.
Demo-to-production gap?
Clean contracts work. Real client documents break everything – OCR errors, non-standard clauses, missing exhibits, and cross-references nobody uploaded.
Hallucinated citations?
Your AI confidently cites cases that never existed. One fabricated precedent in a client-facing brief creates malpractice exposure you can't explain away.
Compliance uncertainty?
ABA Opinion 512, state bar requirements, audit trails, data isolation – you built something that works, but you're not certain it meets the requirements.
Context blindness?
Your RAG system finds the rule in Section 2 but misses the exception buried in Section 10. Technically correct retrieval. Legally wrong advice.
Integration fragility?
Clio, NetDocuments, Westlaw – each integration adds failure points. The system worked fine in isolation. Real production workflows exposed the gaps.
Scaling breaks everything?
Ten users worked fine. A hundred users revealed problems you didn't know existed. The architecture that handled demos can't handle production load.
We've solved each of these problems in production – for intake automation, document analysis, and compliance Q&A systems.
Here's how we solve these problems:
Test datasets built from real production failures
OCR errors, non-standard clauses, multi-document scenarios, adversarial edge cases. The AI gets tested against what it will actually encounter – not sanitized examples.
Citation verification before output reaches users
Post-generation validation checks every cited case and statute against authoritative databases. Hallucinations get caught and flagged before they create liability.
Retrieval that understands legal document structure
Graph-based retrieval tracks definitions, cross-references, and exceptions across sections. The system retrieves context, not just semantically similar chunks.
Compliance-ready architecture from day one
Audit trails, data isolation, access controls, and logging built to meet ABA guidelines and state bar requirements. Not retrofitted after launch.
Integration patterns proven across legal tech platforms
Clio, MyCase, NetDocuments, custom platforms – we know which integrations break and why. Implementation follows patterns that have survived production.
Observability that surfaces problems first
Request tracing, confidence scoring, quality monitoring. Failures get flagged before clients notice. Debugging traces the full reasoning chain.
Most teams ship legal AI that works in demos. We build systems that work when real client data arrives. Get the AI Launch Plan or schedule a consultation to learn more.
Complete legal AI engineering capabilities.
Legal Conversational AI
Intake Automation
Voice and chat agents for client screening and qualification. 24/7 operation with human escalation paths.
Compliance Q&A
Regulatory question answering with inline citations, jurisdiction boundaries, and uncertainty disclosure.
Client Communication
Automated follow-ups, status updates, and document requests. Attorney oversight maintained throughout.
Legal Document & Data AI
Document Analysis
Clause extraction, risk identification, and provision comparison. Handles non-standard contract structures.
Knowledge Retrieval
RAG systems built for legal document hierarchy. Jurisdiction-aware. Definition-aware. Exception-aware.
Research Synthesis
Cross-reference statutes, case law, and internal materials. Every citation verified against source.
Legal AI Infrastructure
Citation Verification
Automated validation against Westlaw, LexisNexis, and internal databases. Hallucinations caught before output.
Compliance Monitoring
Audit trails and access logging that satisfy bar association requirements. Full interaction history preserved.
Production Observability
Request tracing, error categorization, and drift detection. Debug any production failure from logs alone.
Full-Stack AI Integration
Connect AI capabilities to your existing platform, APIs, databases, and workflows. Complete system integration and custom development.
B2B SaaS Platform Development
Complete platform development – interfaces, APIs, databases, authentication, integrations, billing, and other foundational features.
Infrastructure & Deployment
Production deployment, monitoring, scaling, CI/CD pipelines, and security implementation for AI systems and SaaS platforms.
"We've built our entire B2B SaaS platform together, and I genuinely can't imagine working with anyone else"
— Ryan Tabb, Ex-Founder, Bullseye (Exited)
"Their transparency about AI capabilities has been crucial for making informed strategic decisions about our product."
— Kevin M.A. Nguyen, Co-Founder, Proximo AI
Here's how we work together:
Intro Call
We dig into your goals and challenges and figure out if we're the right fit. No sales pitch, but a honest conversation about what needs to happen.
"Softcery's approach is exceptionally thoughtful - they consider the complete business context, not just the immediate technical requirements."
— Kevin M.A. Nguyen, Co-Founder @ Proximo AI
Shaping Phase
We define exactly what gets built, document edge cases, and lock in timeline + budget. This is where we prevent the "6 months later..." nightmare.
"Softcery treated our project with the same care we would - validating every assumption and researching every angle before building."
— Charley Cohen, Director @ TIC.uk, Founder @ Ticitz
Build Phase
We deploy to production in the first weeks and iterate from there. Weekly updates, working functionality you can see and test immediately as we build.
"Softcery combines deep product strategy with technical execution - they don't just follow instructions, they challenge your approach and find better solutions."
— Ryan Tabb, Ex-Founder, Bullseye (Exited)
Launch & Scaling
We finalize the production system, document everything properly, and either hand off cleanly or stick around as your ongoing AI partner. Your choice.
"Softcery was fantastic throughout our re-build of a recently acquired B2B SaaS. Highly recommend in you are in need of high quality AI and SaaS engineering."
— Chris Riley, Acme Studio (Cuppa AI, Bullseye, Experts Ink)
Ready to ship legal AI that works with real client data?
The AI Launch Plan covers the framework we use for legal AI systems – testing strategies, compliance architecture, and production patterns. Or schedule an intro call to discuss your specific requirements.
The Founder's Guide to AI Engineering
In-depth coverage of AI engineering for B2B SaaS founders. Analysis, technical breakdowns, and implementation guides from the field.
Agentic Systems
AI Agents Break the Same Six Ways. Here's How to Catch Them Early.
Works in staging. Fails for users. Six architectural patterns explain the gap, and all of them show warning signs you can catch early.
You Can't Fix What You Can't See: Production AI Agent Observability Guide
Failures you can't reproduce. Error logs that tell you nothing. Three observability pillars solve this: tracing, monitoring, and evaluation.
The AI Agent Prompt Engineering Trap: Diminishing Returns and Real Solutions
Founders burn weeks tweaking prompts when the real breakthrough requires a few hours of architectural work.
Choosing LLMs for AI Agents: Cost, Latency, Intelligence Tradeoffs
Demos work. Production reveals $47 conversations, 2-second pauses, unpredictable failures. Three dimensions help choose.
We Tested 14 AI Agent Frameworks. Here's How to Choose.
Your use case determines the framework. RAG, multi-agent, enterprise, or prototype? Here's how to match.
Voice Agents
How to Choose STT and TTS for Voice Agents: Latency, Accuracy, Cost
Every provider claims low latency and high accuracy. Real differences show up in production. Here's what actually matters.
Real-Time (S2S) vs Cascading (STT/TTS) Voice Agent Architecture
Both architectures work in demos. Different problems emerge in production. Here's what determines the right choice.
Choosing an LLM for Voice Agents: Speed, Accuracy, Cost
Fast models miss edge cases. Accurate models add 2 seconds. Cheap models can't handle complexity. Here's how to choose.
Why Voice Agents Sound Great in Demos but Fail in Production
Understanding why AI voice agents break down is the first step to building a solution that actually works in real life.
Testing Voice Agents: Methods, Metrics, and Tools
Controlled tests pass every time. Real users break agents with accents, noise, and bad networks. Here's what to test for.
Featured
Agentic Coding with Claude Code and Cursor: Context, Memory, Workflows
Agents go in circles without project context. The same agent ships production code daily with proper structure. Here's the system.
8 AI Observability Platforms Compared: Phoenix, Helicone, Langfuse, & More
AI agents fail randomly. Costs spike without warning. Debug logs show nothing useful. Eight platforms solve this differently.
US Voice AI Regulations: TCPA, BIPA, COPPA, HIPAA, & State Privacy Laws
Legal compliance sounds expensive and complex. Most voice AI startups need eight laws and a 5-step framework to ship safely.
11 Voice Agent Platforms Compared: Vapi, Ultravox, Retell, & More
Platforms promise easy setup. Production reveals control limits, concurrency caps, and cost scaling. Match your constraints before choosing.
SOC 2 for Voice AI Agents: Security, Confidentiality, and Quick Wins
Enterprise deals stall without SOC 2. Formal audits cost months and $50k+. Eight steps align your startup now before compliance blocks revenue.