AI features your customers actually use.
We integrate LLMs where they earn their keep, chatbots, agents, copilots, and automations that reduce manual work and delight users.
Everything shipped, nothing missing.
RAG-powered bots that deflect tier-1 tickets and hand off cleanly to humans with full context.
Task-oriented agents that plan, call tools, and complete real work end-to-end with guardrails.
AI-in-the-loop pipelines for email triage, document review, CRM ops, and lead scoring.
In-product AI assistants that understand your data, your users, and your business rules.
Parse PDFs, invoices, and forms into structured data at scale with confidence scores.
Semantic search and personalization using embeddings, hybrid ranking, and re-ranking.
Retrieval-augmented generation, from query to answer.
A production RAG pipeline is more than a prompt. Here's the shape of every AI feature we ship.
Natural language question from your product UI
Semantic search across your indexed knowledge
Model composes a grounded answer with citations
PII filter, output eval, human-in-the-loop check
Delivered token-by-token with source links
The right model, for the right job.
We're not married to any provider. We pick the model that wins on quality, cost, and latency for your workload.
General reasoning, function calling, GPT-4/GPT-5 class quality. Best default for most product features.
Long-context work, safer defaults, structured writing. Excellent for legal, medical, and enterprise.
Llama, Mistral, or Qwen for on-prem or cost-sensitive workloads. Full data sovereignty.
Voice (Whisper, ElevenLabs), embeddings, vision, and rerankers. The right tool for the shape of data.
AI, without the horror stories.
We build AI features with real guardrails, so shipping fast doesn't mean leaking data or losing control of your bill.
- Your data never trains third-party models
- PII redaction before prompts leave your infrastructure
- Fine-grained logging and audit trails on every call
- Cost controls, rate limits, and per-tenant budgets
- Human-in-the-loop for anything high-stakes
- Model portability, swap providers without rewrites
- Prompt injection defenses baked in
- Response caching to cut cost and latency
Estimate your monthly AI spend.
Real usage math based on GPT-4 class pricing. Every AI feature we ship comes with a budget monitor and per-tenant limits.
How we get from kickoff to launch.
Use-case discovery
We map the workflow, ROI signal, and success criteria before touching a model.
Prompt + eval harness
A test set of real inputs with expected outputs, so quality is measurable, not vibes.
Integration + UI
Streaming responses, tool calls, and the human-in-the-loop moments that make it trustworthy.
Guardrails on
Budgets, rate limits, PII filters, and observability wired before you flip the switch.
Improve on real data
We tune prompts, add examples, and swap models as your usage teaches us what works.
Recent work in this practice.
AI questions, practical answers.
Tell us the workflow. We'll tell you if AI belongs there, honestly.
60-minute discovery call. No slides, no sales pitch, just a working session.