AI Agents & Automation
Agents that handle real business workflows — lead qualification, meeting notes, CRM updates — without a human in the loop for every step.
For teams replacing manual workflows with intelligent automation.
Integration architecture
AI agents fail in production when they are designed as chatbots instead of workflow engines. We build agents with explicit state machines — every conversation has defined states, transitions, and fallbacks. When the model is uncertain, the system escalates to a human instead of hallucinating forward.
We have built WhatsApp sales agents with 50% conversion rates, meeting intelligence pipelines processing SEC-compliance for financial advisors, and RAG-powered assistants trained on business owner voice data. The architecture is reliable because the agent knows its boundaries.
What we see in the field
These are the patterns we fix before writing production code.
Agents that go off-script
Free-form conversation agents make commitments they should not and handle edge cases unpredictably. Users stop trusting them.
No CRM or system of record sync
Agent interactions live in a silo. Nothing updates the CRM, nothing triggers downstream workflows.
No human handoff path
When the agent fails or the user escalates, there is no graceful exit. The conversation just ends.
What we build for you
Concrete capabilities—not a generic feature list.
State machine conversation design
Every agent interaction has defined states, valid transitions, and explicit fallback paths — not just a prompt and a hope.
WhatsApp and messaging integration
Twilio or WABA-direct for WhatsApp, with media handling, template messages, and session management.
Meeting AI pipeline
Recall.ai bots, AssemblyAI transcription, LLM processing into structured outputs — summaries, action items, compliance flags.
CRM and calendar sync
Bidirectional sync with Salesforce, HubSpot, Redtail, Wealthbox, and Google Calendar via their APIs.
How we deliver this
A structured path from discovery to something your team can run.
- 01
Map the workflow, not the chat
We define what the agent must accomplish, what data it needs, and where human judgment is required.
- 02
Design the state machine
States, transitions, and fallback behaviors specified before a single prompt is written.
- 03
Build and integrate
Agent logic, LLM calls, third-party APIs, and CRM sync wired and tested with realistic conversation flows.
- 04
Monitor and improve
Conversation analytics, error rates, and escalation patterns reviewed to improve coverage over time.
Outcomes you can expect
- Agents that handle the common cases reliably and escalate edge cases cleanly
- CRM and system of record updated automatically after every interaction
- Human handoff paths that feel intentional, not like failure
- Conversation analytics that show you where the agent is losing users
What we deliver
- State machine agent architecture and conversation design
- Messaging channel integration with fallback and escalation paths
- LLM processing pipeline with structured outputs
- CRM sync and downstream workflow triggers
Who this is for
- Sales teams handling high lead volume via messaging channels
- Operations with repetitive workflows that follow predictable patterns
- Companies with meeting-heavy processes needing structured output from audio
The result
Chatbot agents get built without state machines. They handle the demo scenario and fail on everything adjacent. The team disables the feature after one week of complaints. An agent without a state machine is just an expensive autocomplete.