Production AI agents that use your tools, retrieve company knowledge and execute multi-step workflows under human review.
Production AI agents that use tools, retrieve company knowledge and execute multi-step workflows.
Support workflows
Operations automation
Lead qualification
Internal copilots
Multi-step research tasks
Agent architecture
Tool integrations
RAG layer
Evaluation harness
Monitoring and audit logs
Production deployment
Agents that call internal APIs, databases and SaaS tools to complete real tasks.
Retrieval from documents, policies and internal data — no generic model guesses.
Approve, override and hand off sensitive actions, with full audit logs.
Test sets, logs, traces and regression checks that keep behaviour predictable.
Map the workflow, the tools involved, the users and the actions the agent must never take.
Define tool interfaces, permissions, retrieval layer, review policy and success criteria.
Build the agent on your real data and workflow, validate against a test set.
Deploy, monitor traces and evaluations, iterate on failure modes.
An AI agent that parses Visa and Mastercard rulebooks, predicts interchange fees per transaction and answers internal compliance questions grounded in card-network documentation.
View case study →A 24/7 concierge agent that qualifies inbound leads and books property viewings across WhatsApp and Zillow, with full HubSpot integration so the sales team picks up the conversation where the agent left off.
View case study →A real-time voice agent for inbound customer support — sub-500ms latency, natural turn-taking, deep integration with the existing CRM — reducing load on human agents by ~40%.
View case study →A chatbot generates text. An agent takes actions in your systems — reading data, calling APIs, updating records — under defined guardrails and, where needed, human review.
Yes. Agents are wired to your systems through typed, permissioned connectors — CRM, ERP, databases, internal services or SaaS APIs.
Every sensitive action requires explicit human approval, and every step is logged. Guardrails validate inputs and outputs at each step; an evaluation harness catches regressions before rollout.
Yes. We deploy in your cloud (AWS / GCP / Azure / VPC) or on-premise, including air-gapped environments with self-hosted models.
Book a technical call. We'll review your workflow, data, integrations and constraints, then recommend what is worth prototyping.
