Service 02 / 12
Services de AI Development
AI engineering for products and operations: integrations, agents, automation and AI features, built with evaluations, guardrails and cost controls from day one.
Service overview
Most AI projects stall between a promising demo and a feature people rely on. The gap is engineering: clean data access, retrieval that returns the right context, evaluations that catch regressions, guardrails against prompt injection, and cost and latency budgets that hold up at real traffic.
We build that layer. For SaaS teams, that means AI features and copilots inside your product. For operations, it means agents and automated workflows connected to your CRM, helpdesk, ERP or store. For Shopify merchants and app developers, it means AI apps and merchant tools built on Shopify's APIs.
We work with the major model providers, including OpenAI, Anthropic and Google, and pick the model per task on measured quality, cost and data-handling requirements, not on hype.
Webguru
AI Development
Engineering partner for AI features, agents and automation that hold up in production.
- 01 / 04
AI integration
LLM APIs, retrieval-augmented generation (RAG) over your data, structured outputs and AI features inside existing software.
- 02 / 04
AI agents and workflows
Agents that use your tools through function calling and the Model Context Protocol, with approvals, audit logs and limits.
- 03 / 04
AI lead generation and automation
Inbound capture, enrichment, qualification and routing, with AI where it helps and rules where they're safer.
- 04 / 04
AI for Shopify
AI apps and merchant tools: catalog enrichment, search, personalization, support automation and analytics.
How we build AI that holds up
06 —
Evaluation first
We write the test cases and pass criteria before the prompts, so every change is measured.
Model-agnostic
We choose between providers per task on quality, latency, cost and data-residency needs.
Guardrails built in
Prompt-injection defenses, permission scoping, human approval for risky actions and audit logs.
Cost and latency budgets
Caching, model routing and token limits so unit economics work at real traffic.
Your data stays yours
Least-privilege access, retention settings chosen deliberately and no training on your data by default where providers allow it.
Production engineering
Monitoring, fallbacks, versioned prompts and CI, the same standards as the rest of your software.
Capabilities
- F/01
Retrieval-augmented generation
Chunking, embeddings, hybrid search, reranking and citations over documents, catalogs and knowledge bases.
- F/02
Tool use and agents
Function calling, MCP servers and multi-step workflows that act in your systems with clear limits.
- F/03
Structured outputs
Schema-validated JSON for classification, extraction and routing that downstream code can trust.
- F/04
AI in SaaS products
Copilots, summaries, recommendations and natural-language search inside your app.
- F/05
Automation and integrations
Webhooks, queues and APIs connecting AI steps to CRMs, helpdesks, ERPs and Shopify.
- F/06
Evaluation and monitoring
Eval harnesses in CI, production tracing, quality dashboards and alerting.
Our AI development process
Every AI project moves from a measurable definition of good to a monitored production feature.
- 01
Scope
We pick the one job AI should do first, the success metric and the data it needs.
- 02
Evaluation set
We collect real examples and pass criteria before building, so quality is measurable.
- 03
Prototype
We test models and approaches against the evaluation set and compare quality, cost and latency.
- 04
Build
We build the production version with guardrails, integrations, logging and cost controls.
- 05
Launch and monitor
We release behind a flag, watch real usage and keep improving against the evaluations.
Tools we use
01Models
- OpenAI
- Anthropic Claude
- Google Gemini
- Hugging Face
02Frameworks
- TypeScript
- Node.js
- Next.js
- LangChain
- Vercel
03Data and infrastructure
- PostgreSQL
- Supabase
- Redis
- Docker
- AWS
- Google Cloud
Nous répondons à ce qui compte pour votre entreprise.
Designing and building software where a model does part of the work: LLM features in a product, retrieval over your data, agents that use your tools, or automated workflows. It includes the engineering around the model: data access, evaluations, guardrails, monitoring and cost control.
We work with the major providers, including OpenAI, Anthropic and Google, and with open models where hosting them makes sense. We choose per task after testing quality, latency, cost and data-handling requirements.
Yes. Most of our AI work is integration: adding AI features to an existing SaaS product, CRM, helpdesk, ERP or Shopify store through their APIs, without rebuilding the rest.
We define an evaluation set before building, validate structured outputs against schemas, add human approval for risky actions and monitor quality in production. Changes to prompts or models are tested against the evaluations before release.
We give AI components only the access they need, choose provider settings for data retention and regional processing to fit your requirements, and defend against prompt injection. For UK and EU data we design with GDPR in mind.
It depends on the scope, the integrations and the expected usage, since model costs scale with traffic. We share an estimate after a scoping call.