You probably do not need to train a model
Most companies get more value from integrating existing AI capabilities into the software they already run than from building models of their own.
"Should we build our own model?" is usually the wrong question
Training a model is expensive, slow, and demands data most organisations do not have in usable form. Meanwhile the capability you actually need — read this document, classify this ticket, search this knowledge base — is available through an API today.
An integration layer you own, around a model you can swap
We build a thin layer inside your application that owns prompts, validation, retries, caching, cost limits and evaluation. The provider sits behind it. When a better or cheaper model appears — roughly every six months — you change a configuration value, not a codebase.
Capabilities
LLM API Integration
Provider-agnostic access with retries, timeouts, cost caps and structured output validation.
AI Chatbots
Scoped conversational interfaces that answer from your content, not from the open internet.
AI Assistants
Task-specific helpers embedded where the work already happens.
Document Intelligence
Extraction and classification from PDFs, forms and scans, validated against a schema.
Speech-to-Text
Transcription for calls, meetings and field recordings, with speaker separation.
Text-to-Speech
Generated audio for accessibility and notification use cases.
Image Understanding
Classification, description and extraction from photographs and scans.
Semantic Search
Search that matches meaning rather than keywords, over your own content.
RAG
Retrieval-augmented answers with citations back to the source document.
AI-powered Recommendations
Content and product suggestions grounded in your catalogue and behaviour data.
AI-powered Classification
Routing and tagging at volumes that make manual triage impractical.
AI-powered Extraction
Turning unstructured text into typed records your systems can act on.
AI-powered Summarisation
Condensing long documents and threads, with the source kept one click away.
AI-powered Analytics
Natural-language questions answered against a modelled dataset, with the query shown.
How it fits together
Where the integration layer sits
The layer is yours. The model is a dependency behind it — which is what makes switching provider a configuration change rather than a rewrite.
- 01Existing applicationYour product, unchanged in structure
- 02TOOKLI AI integration layerPrompts, validation, retries, caching, cost limits
- 03AI modelHosted API — replaceable
- 04Business dataRetrieved under existing permissions
- 05Business actionTyped, validated, logged
Systems we build against
Platforms and services TOOKLI actively works with on client engagements.
Web and mobile
- React
- Next.js
- React Native
Backend
- Node.js
- Python
- PostgreSQL
- pgvector
Microsoft
- Power Platform
- Power BI
- Azure AI
- Azure OpenAI
Cloud and model providers
- AWS
- OpenAI
- Anthropic
- Google Gemini
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.
- SaaS products
Adding search and summarisation to an existing app
A capability users ask for, delivered in weeks, without a data science hire.
- Operations
Classifying and routing inbound volume
Tickets, emails and forms triaged consistently at a volume people cannot sustain.
- Professional services
Document extraction into existing systems
Turning contracts and reports into structured records your workflows already understand.
Delivery process
- 01
Feasibility first
A short assessment of whether an API-based capability clears your accuracy bar.
- 02
Define the evaluation set
50–200 real examples with accepted answers, written before any provider is chosen.
- 03
Build the integration layer
Structured outputs, schema validation, retries, caching, cost limits, logging.
- 04
Measure against a baseline
Compared with the simplest alternative — keyword search, rules, or the manual process.
- 05
Ship behind a flag
A small group first, with a rollback that has been tested.
- 06
Monitor and re-evaluate
Providers change models underneath you. The evaluation set runs in CI, permanently.
How we keep it safe and accountable
Engineering practices, not certifications. TOOKLI holds no security certifications and does not claim any.
Provider independence
No provider SDK leaks into your application code. Switching is a configuration change, which also means a price rise is a negotiation rather than a migration.
Structured output validation
Model output is parsed against a schema before anything downstream sees it. An unparsable response is an error, not a surprise in your database.
Cost control
Per-feature budgets, caching of repeated calls, and alerts on spend. AI features fail on unit economics more often than on quality.
Data minimisation
Only what the task needs is sent, redacted where possible, with the processing location documented — which matters for GDPR and Canadian privacy obligations.
Common questions
Related services
AI 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 moreEnterprise AI & Knowledge Systems
Secure enterprise knowledge assistants, RAG systems and internal AI search where access control is enforced at retrieval, not requested in a prompt.
Learn moreAI Product Engineering
Engineering partner for companies building AI-powered products: MVPs, AI SaaS, copilots, search and recommendation systems where the AI is part of the product, not bolted on.
Learn more
Have a product that could use AI?
We will assess feasibility against your accuracy and cost bar, and tell you plainly if an API-based capability is not good enough yet.