Product AI strategy
Workshops and customer research to decide which AI features customers will pay for, how they fit the platform, and what security and data rules apply.
Product & engineering transformation
Build AI into your product and your delivery process
Build AI into your product and your delivery process
without stopping either
We help software teams decide where AI belongs in the product, prototype it quickly, modernise legacy code in increments, and redesign delivery so engineers ship faster with AI assistance.
Book discovery call
Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
How we change
what you ship and how
Rapid prototype sprint
A two to four week sprint that turns one product idea into a working prototype users can test, with a written plan for what production would require.
AI-enabled SDLC redesign
Changes to how your teams plan, code, review, and test with AI assistance, including tooling, guardrails, and measures of delivery speed and quality.
Legacy SaaS modernization
Incremental replacement of fragile modules, frameworks, and infrastructure while the product keeps running, ordered by risk and customer impact.
Product organization redesign
Team structures, roles, and rituals adjusted for faster AI-assisted delivery, so ideas move from discovery to release with fewer handoffs.
Customer-facing AI features
Production builds of AI features inside your product, with evaluation, usage limits, and monitoring that keep quality visible after launch.
Customers asking for AI features you have not scoped?
Bring your top requests and we will help you size them on the call.
Book discovery callIs your product team
facing these pressures?
Competitors announce AI features monthly
Pressure to respond leads to features nobody validated with customers.
Prototypes that never ship
Demos impress in meetings but have no path through security and scale.
Legacy code slows every release
Old frameworks make each change risky and expensive.
AI coding tools without process change
Engineers use assistants, but review, testing, and planning work as before.
How a product transformation
moves forward
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We review the product roadmap, codebase health, and delivery metrics, and interview product and engineering leads.
Deliverables: Product AI opportunities, codebase assessment, delivery baseline
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The strongest idea becomes a working prototype tested with real users, with a production plan alongside it.
Deliverables: Prototype, user feedback, productionization plan
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An FDE pod ships the feature to production and modernises the parts of the codebase it touches.
Deliverables: Production feature, modernised modules, test coverage
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Teams adopt AI-assisted workflows with clear guardrails, measured against the delivery baseline.
Deliverables: SDLC playbook, tooling setup, delivery metrics
Featured success stories
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Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Plan the next AI capability for your product
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Frequently asked questions
How fast can a prototype be ready?
A focused prototype sprint takes two to four weeks. The prototype is real software that users can test, built with production in mind, though it is not production-ready until security, scale, and evaluation work is done.
Do you modernise everything at once?
No. We replace components in order of risk and customer impact while the product keeps running. Full rewrites carry high risk and long delays, so we use them only when a component cannot be improved incrementally.
Will AI coding tools replace our engineers?
No. AI assistance speeds up parts of the work, such as boilerplate, tests, and documentation, but design decisions, reviews, and accountability stay with engineers. The SDLC redesign focuses on making that split explicit and safe.
How do you measure delivery improvement?
We baseline metrics such as lead time, deployment frequency, change failure rate, and review time before changes, then track them monthly. Results vary by team, and we report them honestly, including where improvement is small.
Can you work alongside our product team?
Yes. Pods join your existing product and engineering rituals, and your product managers keep ownership of the roadmap. We bring additional capacity and AI experience rather than a parallel process.
Which stacks do you work with?
Common stacks include JavaScript and TypeScript, Python, Java, .NET, Go, and PHP, on AWS, Azure, or Google Cloud. For older stacks we assess the safest modernization path before proposing changes.