Services · 04
Make the enterprise's own data able to answer.
Most enterprise data is not ready for AI. We build the pipelines, ontologies, knowledge graphs and retrieval layers that turn documents and systems into context a model can use, with lineage and permissions intact.
A data readiness map by use case. Pipelines from SAP, historians, CRMs and document estates. An ontology and knowledge graph your organisation owns and can regenerate from sources. Retrieval that respects roles. Edge deployment where data must stay in the plant or the country.
Entity resolution across systems. Standards alignment where it helps (ISA-95, OPC UA information models). Property-graph or RDF stores with W3C-portable exports. Evaluation on competency questions agreed with the business. Portability by design: you own the ontology, the registry, the mappings and the outputs.
Enterprise knowledge-graph and ontology programmes for manufacturing (capability). Document chat over hundreds of gigabytes of executive documents (unnamed). ⦃param⦄ Universal Knowledge Engine inside ai/Studio.
Data readiness review. Sources, gaps, access boundaries and the first use case, with an architecture and a plan.
Most engagements continue as a dedicated team, embedded with yours, on time and materials.Review our data readinessHow we propose
What you receive before you commit.
Every first step produces the documents an enterprise needs to decide, in a form your architecture, security and procurement teams can review without a meeting.
Business requirements document
the workflow, the baseline, the owner, the measure of success
Approach and architecture
systems touched, data flows, model choices, approval points, deployment topology
Technical proposal
scope, phases, team, assumptions, what is in and what is out
Security controls
identity, perimeter, egress, logging, the questionnaire answered
Infrastructure bill of materials and TCO
what it runs on, what it costs to run
Model cost estimate
tokens, runs, ceilings, and the levers that change them
Delivery plan
sprints, milestones, decisions, and the date you see the first working slice
Every first step produces these, in a form your architecture, security and procurement teams can review without a meeting.
Start with one question →Questions buyers ask
Before you call.
Plain answers to what buyers ask first.
- RAG or knowledge graph?
- Usually both, in that order; a graph earns its place when questions cross systems.
- Who owns the ontology?
- You do.
- Can plant data stay in the plant?
- Yes, with edge deployment and document-first, sensor-light designs where connectivity is poor.
The other five services.
AI product engineering
AI-native products and AI features, from discovery to scale. Product thinking, experience design and full-stack delivery with the model as one component.
First step · AI product opportunity sprint
Product engineering
New products and platforms on web, mobile and cloud: .NET, Python, Node, React, native mobile. Eighteen years of shipping, now with AI in every team's toolchain.
First step · Discovery and architecture sprint
Enterprise AI engineering
Agents, workflows, control towers and knowledge systems integrated with ERP, documents and operational data, governed and traceable.
First step · Production readiness assessment
AI production engineering
Evaluation sets, LLMOps, security controls, observability and cost ceilings: the discipline that keeps an AI system trustworthy after launch.
First step · Architecture review
Modernization with AI
CodeFlo-led migration of existing applications, one reviewable slice at a time, then AI-enablement of what you keep.
First step · Modernization assessmentWhat's next
What needs to work in your world?
A product, a workflow, a knowledge base, a platform that needs to change. Bring the problem. We will work through the engineering and tell you plainly whether it is worth building.
