AI inside a real product, not a chatbot beside one
We build AI-powered products end to end — the application, the AI layer, the evaluation harness and the observability that keeps it honest in production.
The chatbot in the corner does not make it an AI product
Plenty of products have added a chat widget that knows nothing about the user's state and cannot act on their behalf. It demos, it does not retain. AI earns its place when it changes the core workflow — what the product does, not what it can talk about.
Product engineering first, AI as one of the materials
We build the product properly — auth, data model, billing, mobile, observability — and treat AI as one component with its own evaluation harness, cost budget and failure behaviour. That is what makes it survivable when the model changes underneath you.
Capabilities
AI MVP
The smallest product that tests the riskiest assumption, in front of real users.
AI SaaS
Multi-tenant products with auth, billing, per-tenant isolation and usage-based cost control.
AI Mobile Applications
React Native products with on-device constraints and offline behaviour designed in.
AI Copilots
In-product assistance that understands the user's current state and can act on it.
AI Search
Semantic and hybrid search as a core product surface rather than a settings page.
AI Recommendation Systems
Ranking grounded in your own catalogue and behaviour data, measured against a baseline.
AI Workflow Platforms
Products whose core object is a workflow, with AI handling the judgement steps.
AI Agent Platforms
Products that let your own customers configure agents, with the guardrails built in.
How it fits together
From idea to scale
Feasibility sits early and deliberately. Discovering in week two that the accuracy bar cannot be met is a good outcome; discovering it after the launch date is not.
- 01Idea
- 02Product discoveryUsers, jobs, constraints
- 03AI feasibilityCan it clear the accuracy bar at a viable cost?
- 04UX prototypeIncluding the failure and empty states
- 05ArchitectureData model, tenancy, evaluation, cost
- 06MVPReal users, narrow scope
- 07EvaluationMeasured against a baseline
- 08ProductionObservability, rollback, on-call
- 09ScaleCost per user, latency, quality over time
Systems we build against
Platforms and services TOOKLI actively works with on client engagements.
Frontend
- React
- Next.js
- React Native
- TypeScript
Backend and data
- Node.js
- Python
- PostgreSQL
- Supabase
- Vector databases
- pgvector
AI
- LLM APIs
- OpenAI
- Anthropic
- Azure OpenAI
Platform
- Vercel
- AWS
- Azure
- Observability tooling
This list reflects systems we integrate with, not partnerships, resale agreements or certifications. If something you rely on is missing, ask — we will tell you honestly whether we have used it.
What this looks like in practice
Examples of what we can build for each sector. Work we have actually delivered appears in our case studies, with named outcomes.
- Startups
An AI MVP that tests the real assumption
Built to answer whether people want it, not to look finished at a demo day.
- SaaS
An AI layer inside an existing product
Added without destabilising the product that currently pays the bills.
- Marketplaces
Search and ranking as the product
Where match quality is the reason people return, measured rather than assumed.
Delivery process
- 01
Product discovery
Who it is for, what they do today, and what would make them switch.
- 02
AI feasibility
An evaluation set and a measured baseline, before committing to a build.
- 03
UX prototype
Including what the product does when the model is slow, wrong or unavailable.
- 04
Architecture
Data model, tenancy, cost per action, evaluation strategy — written down.
- 05
MVP
Narrow, real, in front of users. Instrumented from the first release.
- 06
Evaluate and iterate
Quality and retention measured together. One without the other misleads.
- 07
Production and scale
Observability, cost controls, on-call, and a rollback that has been rehearsed.
How we keep it safe and accountable
Engineering practices, not certifications. TOOKLI holds no security certifications and does not claim any.
The product works when the AI does not
Every AI surface has a defined degraded state. A provider outage should cost you a feature, not the application.
Cost per action is a design constraint
We model unit economics during architecture. AI products fail on margin at least as often as on quality, and it is much harder to fix afterwards.
Evaluation runs in CI
Prompt and model changes are tested like any other change. A quality regression fails the build rather than reaching users.
You own everything
Your repository, your cloud accounts, your data. Prompts and evaluation sets are assets and are version-controlled alongside the code.
Common questions
Related services
Software Engineering
Custom platforms and internal systems designed to be maintained for years, not abandoned after launch.
Learn moreAI Agents & Intelligent Automation
Agents and automated workflows that connect your data, applications and processes — built with human approval, permission controls and monitoring from the first design.
Learn moreAI Integration
Bring AI capabilities into your existing applications — search, extraction, classification, summarisation and assistants — without building or training models yourself.
Learn more
Building something with AI in it?
Bring us the idea and the accuracy bar it has to clear. We will tell you whether it is buildable today, and what it will cost per user at scale.