Services · 01
AI-native products and features, engineered to ship.
For product teams who need something differentiated in users' hands before the window closes. We find the moment where intelligence removes friction, design the experience around it, and build the application, data and model layers as one product.
An opportunity map ranked by user value and feasibility. An experience prototype people can react to. A reference architecture with model choices, evaluation criteria and cost. A product that ships on a date and scales after it.
Product discovery and user research. Experience design for probabilistic behaviour. Structured outputs, retrieval and tool use where they earn their place. Evaluation sets built from real user tasks. Instrumentation for quality, latency and cost per feature.
Demand forecasting that halved perishable wastage for a hyperlocal grocer (historical account). AI features inside consumer and B2B products for retail, fintech and automotive clients (capability).
AI product opportunity sprint. Opportunity map, feasibility, experience prototype, architecture, and a decision.
Most engagements continue as a dedicated team, embedded with yours, on time and materials.Scope my AI productHow 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 decide whether a feature needs AI at all?
- By testing the user moment first; many winning features turn out to be search, ranking or rules.
- Which models do you use?
- The one the task needs, chosen by evaluation, behind an interface that lets you change it.
- Do we own the code and the models?
- Yes; the architecture avoids dependency on one provider where your requirements permit.
The other five services.
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
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.
