Ops teams with repeated manual work
Your team lives in spreadsheets, inboxes, WhatsApp, and admin panels. We map the workflow before adding AI.
Build AI workflows that survive production.
We connect AI to real data, permissions, review gates, and audit logs — so it helps inside the workflow, not beside it.
- Engagement
- Audit · prototype · operate
- Team
- 2–4 senior engineers
- Stack
- OpenAI · Claude · n8n · pgvector
- Run
- 3–12 weeks
- From
- AI readiness to production ownership
Who this is for
Operators tired of demos. Ready for plumbed-in systems.
If you’ve seen ten AI demos that didn’t survive contact with your data — we know that pain.
Founders who want a company brain
You need answers from documents, CRM notes, tickets, and internal decisions — without trusting a black-box chatbot.
Product teams adding AI features
You need AI inside the product with permissions, logs, evals, and human review. Not a demo that fails after launch.
What we ship
Connected systems with review loops. Not demos.
Every AI feature we ship has three things: real data behind it, a human-review surface, and a cost dashboard.
01
AI systems readiness audit
Workflow maps, data sources, permissions, approval points, and risk areas before implementation.
02
Structured data and RAG pipelines
Clean intake, normalized records, retrieval-ready content, metadata, and source-grounded responses.
03
Permissioned action APIs
Constrained APIs that let AI draft, recommend, or trigger actions only inside approved boundaries.
04
Human-in-the-loop dashboards
Review screens where humans approve, reject, edit, or escalate AI-generated work.
05
Evals and guardrails
Golden test cases, hallucination checks, edge-case tests, and provider-agnostic model evaluation.
06
Audit trails and cost monitoring
Logs for prompts, retrieved context, tool calls, approvals, failures, and cost-per-action.
ClaudeOpenAIn8nTrigger.devLangGraphpgvectorPineconePostgresNext.jsNode.jsVercelAWS
How we engage
Three engagement shapes.
One operating model.
Senior-led in every shape. Pick the one that fits the problem.
AI systems readiness audit
We map the workflow, data, permissions, risks, and automation opportunities. You get a roadmap, not a slide bundle.
Agent prototype sprint
We build one constrained workflow around a real bottleneck — connected data, review screen, logs, and test cases included.
AI Ops retainer
We monitor, improve, and operate the workflow after launch. Prompts, evals, dashboards, and provider changes stay owned.
Sample case
From team reviews to an admin intelligence layer.
An internal proof case. A hockey review platform had the data — reviews, claims, subscriptions — but not yet the operational intelligence on top of it.
Internal proof case · The Hockey Review
Turned reviews, claims, and subscription signals into a team intelligence workflow.
Problem
A hockey review platform had team profiles, reviews, claims, subscriptions, and admin notifications — but the data was not yet structured into operational intelligence.
Approach
We turned reviews, team profile fields, claim status, and subscription signals into a team intelligence workflow with profile completeness, review themes, unclaimed review alerts, and admin approval before outreach.
- Teams scored for profile completeness
- Review themes generated for admin review
- Manual admin actions converted into tracked workflow states
Outcome
Admins get clearer visibility into which teams need data cleanup, which unclaimed teams have reputation activity, and where premium conversion opportunities exist.
Admin intelligence dashboard
Common questions
Things operators usually ask.
No. A chatbot is an interface. We build the workflow underneath it — data, permissions, review gates, APIs, logs, and monitoring.
Transmission open
Let’s turn the scattered into a system that holds.
30 minutes · no pitch · no obligation