One framework.
Four stages. Any industry.
Kaizen's Plan-Do-Check-Act, relabelled for what we actually deliver: Plan finds the problem and prices the work, Do builds the AI, Continuity gets it running in production, and Act compounds the gain and feeds the next Plan.
Plan — find the real problem, and price the work
Plan
The paid client entry point — a Kaizen-led Genba walk and root-cause discovery, productised as a fixed-price, fixed-hours package. No open-ended consulting.
What is at risk, what to do about it, who owns it, and by when.
Failure Mode and Effects Analysis — where the process fails, how badly, and how likely.
Three deliverables, every time. Plan is what tells Do exactly what to build and why it is worth building.
Do — build the AI for the parts that compound
Do
Five core offerings, each running live — open any one to see it working. Which of them you need comes out of Plan, not out of a sales pitch.
Answer, screen and route calls in the language your staff and customers actually use — handled without a hold queue.
See it live ↗The handoffs Plan mapped — approvals, scheduling, escalations — running themselves, with a human in the loop only where it matters.
See it live ↗Invoices, contracts, inspection forms and intake — read, checked against policy, and filed correctly the first time.
See it live ↗For processes with more than one moving part — a team of agents, each doing one job well, coordinated the way your own team is.
See it live ↗Ongoing monitoring, evaluation and upgrade retainer — so what was built keeps compounding instead of drifting.
See it live ↗Continuity — built is not the same as running
Continuity
Two components we always deliver ourselves, and two optional add-ons delivered through partners — never presented as in-house work.
The hosting, data pipes and integrations the build actually needs to run — sized for the workload, not over-built.
Staged rollout, staff walkthrough and go-live — so day one in production looks like the demo did.
Flags equipment failure before it happens, not after. Delivered via partner.
Connects sensors, kiosks and on-site devices into the same loop. Delivered via partner.
Act — compound the gain, then start again
Act
Three offerings. Act does not end the engagement — it raises the baseline and feeds the next Plan.
Every system finally speaking to every other system — one source of truth, not five spreadsheets that disagree.
HSE reporting that writes itself from what the agents already see, instead of a person reconstructing it after the fact.
Frontline AI adoption — the shift and the site crew actually using what was built. Competitors target enterprise and office-worker change management; this is the gap they leave.
PLAN → DO → CONTINUITY → ACT ↻ — ten core offerings, two partner-delivered add-ons, all entered through Plan. Thirteen in total, one continuous engine.
The stack behind it
The same components across every build — model-agnostic by design, so a model swap is a config change, not a rebuild.
LangGraph, OpenAI Agents SDK, Anthropic tool use, CrewAI, Microsoft Copilot Studio
Model-agnostic — swapping models is a config change, not a rebuild
pgvector, Pinecone, Qdrant, Weaviate — hybrid semantic + keyword search
Deepgram, LiveKit, Vapi, Retell
LangSmith, Langfuse, Braintrust
AWS Bedrock, Azure AI Foundry, Google Vertex AI, on-prem/hybrid
Direct API, n8n, Make, MCP servers
LlamaParse — turns messy PDFs, scanned forms and complex tables into structured data agents can reason over
NeMo Guardrails — defends against prompt injection and validates outputs before they reach your systems
Temporal — long-running, multi-step workflows survive failures and patchy connectivity
The four stages do not change with the sector. See the same framework applied in yours on Industries.
Turn your Kaizen findings into working AI.
Any industry, one continuous improvement engine.