Most teams have generic chat copilots that summarize content. We build agents that actually do the work — read your inbox, run your CRM, ship code, and close tickets. Production-grade, observable, with safety rails on every step.
Why teams choose us.
Model-Agnostic Expertise
We pick the right model per task — frontier APIs, fine-tuned open-weights, or hybrid. No vendor lock-in.
Safety & Guardrails First
Eval harnesses, prompt-injection defenses, PII redaction, and human-in-the-loop wherever it matters.
Fast Time to Value
Working prototype in 2 weeks, production deployment in 4–8 weeks. Weekly demos so you steer.
Measurable ROI
Every agent ships with metrics — cycle-time saved, deflection rate, $/task. We instrument the win.
The full menu.
Custom AI Agents & Copilots
- Multi-step planners with tool use
- Memory + context management
- Voice + chat interfaces
- Slack, email, web embeds
RAG & Knowledge Systems
- Document ingestion pipelines
- Hybrid keyword + vector search
- Citation-grounded answers
- Re-ranking + freshness controls
LLM API & Tool Integration
- OpenAI, Anthropic, Gemini, Llama, Mistral
- Function calling + structured outputs
- Streaming + caching
- Cost + latency observability
Workflow Automation (n8n / LangGraph)
- Event-driven orchestration
- Human approval gates
- Retry + fallback logic
- Audit trails for every run
LLM Ops & Evaluation
- Eval harnesses + golden-set tracking
- A/B model comparisons
- Drift + regression alerts
- Prompt-injection red-teaming
Our process.
Discovery & Use Case Mapping
We map the workflow you want to automate. Score it on ROI, risk, and feasibility before building anything.
Architecture & Model Selection
Pick the model, vector DB, and orchestration layer. Lock the eval set so we know when we're done.
Build, Evaluate & Harden
Iterate weekly. Adversarial testing for prompt injection, PII leakage, and tool-misuse. No surprises in prod.
Deploy & Scale
Ship with monitoring. Track cost, latency, win rate. Quarterly model migrations baked in.
Shipped. Measured. Receipts kept.
AI customer-support agent for a B2B SaaS handled 73% of tier-1 tickets autonomously. Headcount reallocated to product work, not support backfill.
RAG-grounded sales-research agent shipped from spec to working demo in two weeks. Closed three enterprise deals on the back of the demo.
Cost-aware prompt design + GPT-3.5/GPT-4 mixed routing kept per-call cost under the unit-economics ceiling. Same quality as a GPT-4-only build at 4x the price.
What we build with.
Honest about who this is for.
This will be a fit.
- You have a real workflow to automate — not a vague 'we should do AI' mandate
- You can name the cost ceiling (per call, per user, per month)
- You want production-grade with evals and monitoring, not a demo
- You're OK with human-in-the-loop gates on destructive actions
Honestly — not our zone.
- —You want a chatbot that just answers FAQs (use Intercom Fin, not us)
- —You can't articulate the workflow or what success looks like
- —You expect AI to replace thinking, not augment it
Common questions, straight answers.
See Agentic AI Development in production
Audited engagements where this service shipped real outcomes — costs, timelines, and stacks documented.