Claude · Agents
AI agents that actually do the work.
Purpose-built agents on Claude, GPT, or open-source, with tool-use, retrieval, and memory wired into HubSpot, your data warehouse, and the rest of your stack.
- Claude · GPT · open-source
- Production-grade, not demo
- Human-in-the-loop where it matters
- Production
- Not just demos
- Tool-using
- Real actions, not chat
- Evaluated
- Golden sets & regressions
- Governed
- Guardrails & audit trail
The problem
Everyone builds AI demos. Almost nobody ships agents that hold up.
Chat isn't a workflow
A chatbot answers questions. A real agent takes multi-step actions across your systems, and most projects never get there.
No grounding
The model hallucinates your product, your policies, and your data because nothing retrieved is trusted.
Fragile tool use
Naive function-calling breaks the moment a real user does something unexpected.
No evaluation
You have no idea whether the agent is getting better or worse, because nothing's tested.
The solution
Agents engineered like software.
Model-agnostic architecture
Claude, GPT, or open-source, chosen per use case, swappable when the market shifts.
Grounded retrieval
RAG on your own data, with citations, freshness rules, and hybrid retrieval.
Reliable tool use
Typed schemas, retries, and fallbacks. Agents that fail gracefully instead of hallucinating actions.
Continuous evaluation
Golden test sets, regression tracking, and human review of edge cases before every release.
What's included
What a real agent looks like.
PILLAR 01
Architecture & model choice
The right model per task.
- Claude, GPT, Gemini, or open-source
- Cost, latency, and quality trade-off analysis
- Multi-model routing where it helps
- Swappable model layer
PILLAR 02
Tool use & integrations
Actions, not just answers.
- Typed function-calling with retries
- HubSpot, Slack, Gmail, Stripe, custom APIs
- Async and long-running workflows
- Human approval gates
PILLAR 03
Retrieval & memory
Grounded in your world.
- Vector + keyword hybrid retrieval
- Freshness and permission-aware search
- Per-user memory with expiry
- Citations in every response
PILLAR 04
Guardrails & evaluation
Shippable to real users.
- Prompt-injection defense
- Policy filters and PII handling
- Golden test sets and CI evals
- Human review of red-flag cases
Process
Five stages. Ship, then iterate.
- 01
Scope
Nail the job-to-be-done, the user, and the success metric before touching a model.
- 02
Prototype
Fast build in days to prove the loop works end-to-end.
- 03
Harden
Tool schemas, retries, guardrails, and evaluation harness added.
- 04
Roll out
Staged rollout to real users with monitoring and human review.
- 05
Compound
Weekly eval reviews, prompt iteration, and quarterly re-architecture.
READY WHEN YOU ARE
Ship an agent that earns its keep.
Tell us the workflow you'd hand to an agent. We'll come back with an architecture and quote within one business day.