Technologies
Ship AI features that pass evals — not demos
Most AI projects look great in a notebook and break in production. We build LLM and ML systems with evals from day one, guardrails before launch, and observability after — so the thing you demo is the thing your customers use.
What We Build
Production AI systems, not Jupyter notebooks
LLM Integration
Wire GPT, Claude, Gemini, or open-source models (Llama, Mistral) into your product with streaming, tool use, and structured outputs.
RAG Systems
Retrieval-augmented generation over your private data. Vector DBs (Pinecone, Weaviate, pgvector), reranking, and hybrid search that actually retrieves the right thing.
Agentic Workflows
Multi-step agents with tool use, planning, and state. Built on MCP, LangGraph, or custom orchestration — with circuit breakers and human-in-the-loop.
Evals & Guardrails
Automated evaluation suites, hallucination detection, PII redaction, prompt injection defense. You ship on evidence, not vibes.
Fine-Tuning & Distillation
LoRA fine-tuning for domain accuracy. Distillation to cut latency and cost. Bring your own data or we curate it.
Predictive ML
Classical ML where it still wins: forecasting, churn, fraud, anomaly detection. Built on scikit-learn, XGBoost, or PyTorch — not an LLM hammer for every nail.
How We Work
Eval-first, guardrails-before-launch. The opposite of 'demo, then panic.'
Define the eval
Before writing a line of code, we agree on what 'good' means: accuracy targets, latency SLOs, cost ceilings, refusal policy. No eval, no project.
Data audit
Inspect retrieval corpus, labeling quality, distribution drift. Most LLM features fail because the data is wrong, not the model.
Build the first version
Pick the cheapest model that could plausibly work. Wire it up end-to-end. Resist premature fine-tuning.
Run the eval
Score against the rubric defined in step 1. Find the failure modes. Iterate on prompt, retrieval, chunking, then model.
Add guardrails
Prompt injection defense, PII redaction, output validation, rate limits, cost caps. Ship the safety layer before launch, not after the incident.
Ship + monitor
Production observability for token usage, latency, eval drift, user feedback. Retrain or swap models when the data tells you to.
Define the eval
Before writing a line of code, we agree on what 'good' means: accuracy targets, latency SLOs, cost ceilings, refusal policy. No eval, no project.
Data audit
Inspect retrieval corpus, labeling quality, distribution drift. Most LLM features fail because the data is wrong, not the model.
Build the first version
Pick the cheapest model that could plausibly work. Wire it up end-to-end. Resist premature fine-tuning.
Run the eval
Score against the rubric defined in step 1. Find the failure modes. Iterate on prompt, retrieval, chunking, then model.
Add guardrails
Prompt injection defense, PII redaction, output validation, rate limits, cost caps. Ship the safety layer before launch, not after the incident.
Ship + monitor
Production observability for token usage, latency, eval drift, user feedback. Retrain or swap models when the data tells you to.
Where We Start
The shipping-shaped engagements teams ask us for most
AI Chat & Copilot Features
Embed a domain-aware chat or copilot into your existing product. Streamed responses, tool use, citation, and feedback loops.
Internal Knowledge Search
RAG over your wiki, docs, tickets, contracts, or codebase. Authenticated, audited, and tuned for your team's vocabulary.
Document Intelligence
Structured extraction from PDFs, scans, and emails. Forms, invoices, KYC, contracts — with confidence scores and human review queues.
Custom Fine-Tuned Models
Open-source models fine-tuned on your data for domain accuracy, latency, or cost wins versus frontier APIs.
Computer Vision
Defect detection, OCR, quality control, identity verification. Edge or cloud inference depending on latency budget.
Predictive Analytics
Forecasting, churn, fraud, lead scoring, demand planning. Classical ML where it outperforms LLMs on accuracy and cost.
Common Questions
Frontier API (OpenAI/Claude/Gemini) or open-source (Llama/Mistral)?
What's a good eval?
How do you handle hallucinations?
Do you fine-tune?
What about cost?
Domains we've shipped in
Have an AI feature stuck at the demo stage?
We do the evals, retrieval tuning, and guardrail work that turns prototypes into production.
Related Solutions
AI Agents
Agentic workflows with tool use, MCP, planning, and human-in-the-loop.
Data Engineering
Snowflake, Databricks, BigQuery, dbt, Airflow — modern data stack from ingest to activation.
Enterprise Blockchain
Supply-chain provenance, tokenized assets, settlement rails — audited and production-grade.