AI Solutions

AI that survives contact with production.

Lawbotic Labs designs and builds AI agents, retrieval-augmented assistants, and multimodal pipelines engineered for enterprise data, enterprise constraints, and real users.

What we build

Six things we ship for AI engagements.

01

AI agents & multi-agent systems

Goal-driven agents that plan, call tools, and hand off work. Multi-agent orchestration for workflows where one model isn't enough.

02

Production RAG pipelines

Document ingestion, chunking, embedding, hybrid retrieval, and evaluation harnesses. Built for accuracy in regulated environments.

03

AI assistants & chatbots

Domain-specific assistants embedded into your product or internal tools, with guardrails, citations, and human-in-the-loop fallback.

04

Multimodal systems

Text, voice, and vision pipelines for enterprise workflows. Audio transcription, image understanding, structured extraction at scale.

05

LLM integration into existing apps

Add AI features to your existing stack without a rewrite. Streaming, function calling, fallbacks, observability included.

06

Evaluation & observability

Eval harnesses, prompt management, regression tracking, latency and cost dashboards. So you can ship AI changes with confidence.

Engagement model

Four steps. No dark periods.

Most AI projects fail in scoping and silently die in handover. We've built the engagement to make both unavoidable.

STEP 01

Discovery

Map the problem, the data, the constraints. Identify the single outcome that matters. We say no to projects we can't make a real dent in.

STEP 02

Spike

A small working slice in one to two weeks. Real model, real data, real users if possible. Value before scope.

STEP 03

Build

Production engineering: types, tests, evals, observability, rate limits, secrets. Weekly demos, real artifacts every Friday.

STEP 04

Handover

Documentation, runbooks, eval harnesses, and engineer training. Your team owns it. We stay on call as long as you need.

Our stack

Picked for the outcome, not the resume.

We use what's known to work in production. Swapping providers is cheap when the eval harness is solid.

Models

Claude GPT-4o Gemini Llama Mistral

Frameworks

LangChain LangGraph LlamaIndex Vercel AI SDK Anthropic SDK

Vector

Pinecone pgvector Upstash Vector Weaviate

Runtime

Vercel AWS Lambda Cloudflare Workers Modal

Eval

Braintrust Langfuse Custom harnesses
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