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.

Add AI to Your ProductDiscuss Feasibility
Le problème

"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.

Notre approche

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.

Ce que nous construisons

Nos capacités

  • 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.

Architecture

Comment tout s'articule

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.

  1. 01Existing applicationYour product, unchanged in structure
  2. 02TOOKLI AI integration layerPrompts, validation, retries, caching, cost limits
  3. 03AI modelHosted API — replaceable
  4. 04Business dataRetrieved under existing permissions
  5. 05Business actionTyped, validated, logged
Intégrations

Systèmes avec lesquels nous travaillons

Plateformes et services que TOOKLI utilise activement sur ses missions.

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

Cette liste indique les systèmes que nous intégrons — ni partenariats, ni revente, ni certifications. Si un outil que vous utilisez n'y figure pas, demandez-nous : nous vous dirons franchement si nous l'avons déjà mis en œuvre.

Cas d'usage

À quoi cela ressemble concrètement

Des exemples de ce que nous pouvons construire par secteur. Les projets réellement livrés figurent dans nos études de cas, avec des résultats chiffrés.

  • 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.

Notre méthode

Processus de livraison

  1. 01

    Feasibility first

    A short assessment of whether an API-based capability clears your accuracy bar.

  2. 02

    Define the evaluation set

    50–200 real examples with accepted answers, written before any provider is chosen.

  3. 03

    Build the integration layer

    Structured outputs, schema validation, retries, caching, cost limits, logging.

  4. 04

    Measure against a baseline

    Compared with the simplest alternative — keyword search, rules, or the manual process.

  5. 05

    Ship behind a flag

    A small group first, with a rollback that has been tested.

  6. 06

    Monitor and re-evaluate

    Providers change models underneath you. The evaluation set runs in CI, permanently.

Sécurité et fiabilité

Comment nous garantissons sécurité et traçabilité

Des pratiques d'ingénierie, pas des certifications. TOOKLI ne détient aucune certification de sécurité et n'en revendique aucune.

  • 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.

Questions fréquentes

Services associés

  • 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.

    En savoir plus

  • Enterprise 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.

    En savoir plus

  • AI 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.

    En savoir plus

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.

Discuss feasibility