Build and ship
Ship an AI workflow that runs in your real environment — not just in a demo.
A prototype that works in a sandbox is not a production system. We build the full stack: agent, retrieval, integrations, approval gates, audit trail, and observability — deployed in the tools and environment your team actually uses.
Best for
- Teams that already know the workflow and want to ship it in production
- Companies moving beyond prototypes and internal demos
- Operations or product teams that need integrations, controls, and measurement from day one
Choose this when
Choose this when the target workflow is clear and the next step is to build a production system, not run another experiment.
The problem
What we build
Agent design and implementation
We design and build the agent: the model, the prompt structure, the tool calls, and the decision logic — tuned to your specific workflow.
Retrieval and context layer
We connect the agent to the context it needs: policies, customer records, runbooks, history — permissioned, fresh, and cited in every output.
Integration with your stack
We wire the agent into the tools your team already uses — Slack, Jira, Zendesk, Notion, CRM, email, internal APIs — not a separate interface.
Approval gates and human controls
We define exactly which actions the agent executes directly, which queue for human approval, and what triggers an escalation — before the pilot starts.
Observability and evals
We instrument the workflow from day one: action logs, acceptance rates, latency, and the one metric that proves the workflow is doing its job.
Systems connected
- Slack, Microsoft Teams, Google Chat
- Jira, Linear, GitHub, GitLab
- Zendesk, Intercom, Freshdesk
- Notion, Confluence, Google Drive, SharePoint
- Salesforce, HubSpot, and custom CRMs
- Internal APIs and databases (via scoped, sandboxed access)
Where humans stay in control
- Human-in-the-loop review for all actions above a defined risk threshold
- Agent outputs are drafts or proposals until a human confirms them
- Escalation paths defined and tested before go-live
- Rollback conditions established upfront — the pilot can be stopped cleanly
Outcomes
< 6 weeks
from kickoff to a working pilot in production
Measured
against a real business metric from week one
Survivable
with approval gates, audit trail, and clear human controls
Related services
Support and operations
Support & Ops Agents
Choose this when the workflow is queue-driven and the biggest pain is triage, routing, draft replies, or escalation handling.
Learn more→
Knowledge and retrieval
Internal Knowledge Brain
Choose this when the main bottleneck is fragmented knowledge and the cost of work is in searching, reconstructing, and re-explaining context.
Learn more→
Governance and compliance
AI Governance Layer
Choose this when the workflow touches regulated data, high-risk actions, or enterprise systems that require explicit approvals and auditability.
Learn more→
How this plays out
AI Support Triage Agent
Tickets sat untouched for hours, and a third were misrouted on the first pass.
63%
less time to first response
Read the full story→
TechnologyEngineering Backlog Agent
Every bug ate 1-3 hours of investigation before an engineer could even start the fix.
~15 hrs
engineer time returned per month
Read the full story→
TechnologySlack/Chat-Ops Async Agent
Small "can someone..." requests piled up and always landed on the same two or three people.
~25/week
ad-hoc requests handled directly in Slack, without pulling someone off their work
Read the full story→
Start small, build seriously