Services · 05
The discipline that keeps an AI system trustworthy after launch.
For teams with AI already in production, or about to be. Evaluation, LLMOps, security, observability and cost: the work that decides whether the system is still right in month six.
Evaluation sets that reflect the business, run before every release. Traces for every run, with quality, latency and cost visible. Security controls written down and reviewable. A release process that makes the next model update a tested change.
Task-based evaluation design. Regression checks on prompts, models and retrieval. Outbound-only and edge architectures for strict perimeters. Cost ceilings per run and per tenant. Incident diagnosis from traces. Vendor-assessment and security-questionnaire responses as standard deliverables.
Security control sets, cross-perimeter API surfaces and infrastructure bills of materials delivered for enterprise AI programmes in manufacturing and electronics (capability). ai/Studio's budget enforcement and trace model.
Architecture review. Your system against six lenses, with findings you can act on.
Most engagements continue as a dedicated team, embedded with yours, on time and materials.Review an architecture with usHow 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.
- How do you measure whether an agent is good?
- Against cases the business agreed matter, with a pass rate the release gate enforces.
- What does a run cost?
- You will know, per run, with a ceiling.
- Can you take over a system someone else built?
- Yes; the review is designed for exactly that.
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
Data and knowledge engineering for AI
Pipelines, ontologies and knowledge graphs, permission-aware retrieval and edge deployment, so the enterprise's own data can answer questions and drive agents.
First step · Data readiness 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.
