Start a projectStart a projectFrom retrieval-augmented generation to autonomous agents, we build AI systems that go beyond demos. Production-grade pipelines, guardrails, and evaluation harnesses included.
Four pillars of enterprise AI engineering — each battle-tested across regulated industries.
Ground LLM outputs in your proprietary data with vector search, hybrid retrieval, and citation-backed responses that auditors can trace.
Multi-step agents that reason, call tools, and complete complex workflows — with human-in-the-loop checkpoints where you need them.
Embed AI assistants directly into your product — trained on your docs, your codebase, your domain language.
When prompt engineering hits a ceiling we fine-tune open-weight models and build eval suites so you can measure what matters.
The models, frameworks, and infrastructure we use to ship reliable AI.
Reduce contract review from hours to minutes with clause extraction, risk scoring, and redline suggestions grounded in your playbook.
AI agent that resolves L1 tickets by searching knowledge bases, executing actions via API, and escalating edge cases to humans.
Map clinical notes to ICD-10 and CPT codes with retrieval-backed suggestions, achieving 94%+ accuracy with physician-in-the-loop validation.
Ingest earnings calls, SEC filings, and news feeds to generate investment summaries with source citations and sentiment scores.
Agentic system that monitors inventory levels, predicts demand spikes, and auto-generates purchase orders for procurement review.
Semantic search across Confluence, Notion, Slack, and Drive — with role-based access controls and audit logging for compliance.
Retrieval-augmented generation with source citations — ready for audit trails.
import { ChatAnthropic } from "@langchain/anthropic"; import { createRetrievalChain } from "langchain/chains/retrieval"; const llm = new ChatAnthropic({ model: "claude-sonnet-4-20250514", temperature: 0, }); // Ground responses in your vector store const chain = await createRetrievalChain({ llm, retriever: vectorStore.asRetriever({ k: 8 }), returnSourceDocuments: true, }); const result = await chain.invoke({ input: "Summarize Q4 revenue risks", });
It depends on your constraints. We benchmark multiple providers against your specific use case, measuring accuracy, latency, cost, and compliance requirements. Many clients end up with a multi-provider strategy: a frontier model for complex reasoning and a smaller model for high-volume, low-latency tasks.
We layer multiple guardrails: retrieval grounding with source citations, output validation schemas, confidence-threshold routing, and automated eval suites that catch regressions before deployment. No single technique is enough — it's the combination that makes AI reliable.
Absolutely. We integrate with whatever you have — S3 buckets, Snowflake, Postgres, Elasticsearch, Confluence, SharePoint. Our embedding pipelines handle ingestion, chunking, and incremental updates without replacing your data layer.
We set up monitoring dashboards (latency, cost, accuracy drift), automated eval pipelines, and feedback collection. Monthly reviews analyze production performance and retrain or re-prompt as needed. We can hand off to your team or provide ongoing managed support.
A focused RAG pipeline or copilot MVP takes 4-6 weeks. Multi-agent systems with complex tool integrations typically run 8-12 weeks. We always start with a 1-week discovery sprint to validate feasibility and define success metrics.
Let's turn your AI ambitions into production systems that deliver measurable value.
Start your AI project