Services

Five ways we turn AI into working systems.

From a focused discovery engagement to production-grade implementation and AI quality engineering. Every engagement has clear scope, milestones and acceptance criteria.

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AI Readiness Scan

Free

What you get: A read-only scan that scores your repository on six areas of AI readiness. Every score points at evidence in your code or settings, and anything we can't see, we say we can't see.
Deliverables: Scored report on six areasEvidence for every findingRanked list of fixes
Commercial model: Free

AI Readiness Audit

Full picture

What you get: Builds on the scan with team interviews and a review of tool settings and permissions, then turns the findings into a 30/60/90-day plan with ready-to-merge fixes. No productivity percentages or ROI forecasts: we measure the starting point and show how it changes.
Deliverables: Team interviewsSettings and permissions review30/60/90-day planReady-to-merge fixes
Commercial model: Fixed fee, three weeks

AI Strategy & Architecture

Discovery

What you get: A focused AI opportunity assessment, technical architecture, feasibility review and implementation roadmap.
Deliverables: Use-case prioritisationProposed architectureRisk and evaluation planImplementation phasesIndicative costs
Commercial model: Fixed-fee discovery engagement

Agentic AI & LLM Development

Core offering

What you get: AI agents that plan tasks, use tools, evaluate results and collaborate to complete real workflows.
Deliverables: Working prototypeModel and tool integrationEvaluation testsDocumentationDeployment plan
Commercial model: Milestone-based implementation project

AI Infrastructure & Integration

Implementation

What you get: Local-first or cloud-hosted LLM environments, Docker deployment, API integration, MCP connectivity and workflow orchestration — connected to tools like Jira and Zephyr.
Deliverables: Deployment architectureConfigured servicesIntegration testsMonitoring approachHandover documentation
Commercial model: Implementation fee plus optional maintenance retainer

AI Quality & Reliability

Differentiator

What you get: AI evaluation frameworks, regression testing, prompt and model testing, output validation and quality gates for AI applications — built on a Senior QA Automation background.
Deliverables: Evaluation frameworkPrompt and model regression suiteOutput validation rulesQuality gates in CIReliability report
Commercial model: Assessment, implementation project or ongoing testing engagement

AI Product Engineering

Build

What you get: AI-enabled prototypes and MVPs for startups and established businesses.
Deliverables: Problem definitionProduct requirementsArchitectureWorking softwareUser testingRoadmap to production
Commercial model: Staged development contracts, with licensing or subscription where suitable

Who we work with

Built for teams that need AI to actually work.

We focus on three groups where practical AI engineering makes the biggest difference.

SMEs and growing businesses

Automate repetitive operations, document handling, customer workflows and reporting.

Technology companies and startups

Build AI features, LLM integrations, internal AI tools and MVPs.

Enterprise technology teams

Evaluate AI use cases, prototype integrations, improve AI quality assurance and deploy governed AI workflows.

Our approach

How every engagement runs.

1. Discover

Understand your goals, workflows, data and constraints. Prioritise the use cases worth building.

2. Build

Design the architecture and build a working prototype integrated with your real systems.

3. Validate

Test, evaluate and measure. Every AI output gets quality gates, not just a demo.

4. Scale

Deploy responsibly, hand over cleanly and turn what works into reusable products.

Pricing

Clear scope. Milestone payments.

Quotes depend on scope, security requirements, integrations, delivery time and acceptance criteria. We start with a fixed-fee discovery so you know exactly what you're buying before any build begins. We can contract in EUR, USD or KES.

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Have a workflow AI could improve?

Tell us about the problem. We'll suggest a practical path — starting with a focused discovery engagement.

Start a conversation Ask Jay