Conversational copilots
In-product assistants grounded in your domain knowledge — with tool use, memory, and human-in-the-loop where it matters.
Capability · AI
Applied AI, embedded where it matters
LLM applications, retrieval-augmented generation, fine-tuned vision and voice models, and AI copilots — built into your product, not bolted on as a demo.
Our point of view
AI that does the work — not AI that does a tour
The gap between an impressive AI demo and a useful AI product is enormous. The demo cherry-picks. The product has to handle Tuesday at 4pm with messy real data and a frustrated user.
We engineer for Tuesday. That means evals before features, guardrails before glamour, and observability from the first deployment.
- Production LLM applications & chat interfaces
- RAG over your documents, databases, and tools
- Fine-tuned models for specialized domains
- Computer vision pipelines (detection, segmentation, OCR)
- Voice agents & speech understanding
- AI copilots embedded inside existing workflows
- Evaluation harnesses, observability, and continuous tuning
AI · LLM
GPT · Claude · PyTorch · LangChain
What We Build
AI patterns we ship to production
RAG over your docs
Question-answering across knowledge bases, contracts, support history, and codebases with citations and source attribution.
Computer vision
Object detection, segmentation, OCR, defect inspection, and custom-trained models for your imagery and your domain.
Voice agents
Real-time speech-to-speech systems with low-latency turn-taking, custom voices, and grounded knowledge — for support and beyond.
Document & data extraction
Turn invoices, contracts, lab reports, and forms into structured data your downstream systems can actually use.
Agentic workflows
Multi-step AI agents that plan, call tools, and execute — with audit trails, cost controls, and meaningful evals.
Engineering Discipline
What separates production AI from demo AI
01
Evals before features
We build the eval harness before we tune the prompts. Otherwise you're flying blind.
02
Guardrails & safety
Input filters, output validation, jailbreak resistance, PII redaction — wired in, not afterthoughts.
03
Cost & latency budgets
Every request priced and timed. Model routing where it matters. No surprise bills.
04
Observability throughout
Trace every step, log every tool call, replay every failure. Debugging AI without traces is malpractice.
05
Privacy & data handling
SOC 2-aligned data pipelines, optional self-hosted models, zero-retention configurations where required.
06
Continuous tuning
Real production traffic feeds the next round of evals and improvements. The model gets better the longer it runs.
Common Questions
What clients usually ask
Should we use OpenAI, Anthropic, or self-hosted?
Depends on cost, latency, privacy, and capability requirements. We benchmark on your task before recommending. Often the right answer is a router that picks the cheapest sufficient model per request.
Do we need to fine-tune?
Usually not first. RAG and good prompts solve most problems. Fine-tuning makes sense for specialized output formats, domain jargon, or when latency / cost demands a smaller model.
How do you handle hallucinations?
A combination of grounding (RAG with citations), output validators, evals on factuality, and UX that makes the model's confidence visible. There's no single switch — it's a system.
Can the AI run on-device?
Increasingly yes — for vision, speech, and small language models. We deploy on-device when it serves privacy, latency, or offline requirements; we use cloud when capability matters more.
Have a workflow that AI could transform?
Tell us what's painful, slow, or repetitive. We'll tell you whether AI is the right answer — and what shipping it actually costs.