AI integration

AI in your product, actually useful. Not as a gimmick.

Conversational assistants, RAG over your docs, autonomous agents, vision, generation. GPT-5, Claude Opus, Gemini, Llama, Mistral — we pick the right model for the right problem.

Start an AI project See use cases

GPT-5 · Claude · Gemini · open-source
RAG, fine-tuning, agents, function calling
Vector DB, LangChain, LlamaIndex
GDPR compliance & EU sovereignty available

The context

Why 2026 changes everything.

In three years, generative AI has moved from viral demo to industrial infrastructure. GPT-5 reasons on complex scientific problems, Claude Opus 4.8 maintains a 1-million-token context window without losing track, Gemini 3 Pro handles video, audio and code in the same request. The frontier of what's possible has shifted faster than most product teams can absorb.

The result: most companies either ignored the wave or shipped a half-baked chatbot and abandoned it. In between, there's massive untapped ground — real integrations that cut ops costs, accelerate sales cycles or create products competitors can't yet imagine. That's where we work.

Our approach is neither techno-evangelist (« AI everywhere ») nor techno-skeptic (« let's wait for the dust to settle »). We start with a precise, measurable use case, prove value in 2-4 weeks with a testable MVP, and industrialise only when ROI is proven. No hype, no bullshit.

$4.4T

AI value generated by 2030 (McKinsey)

73%

Companies using generative AI in 2025 (vs 33% in 2023)

$0.20

Average cost of a RAG query in production

2-4 wks

From brief to first functional MVP at OmniX

Real-world cases

Six workloads we ship to production.

No gimmick AI. Here are the architectures that justify the investment, each with the starting problem, what we build, and measured impact.

Business conversational assistant

Support, sales, onboarding — a sourced virtual human.

Problem

Your support teams drown in repetitive questions. Your sales reps spend 40% of their time hunting for product info. Your new hires take 3 months to become autonomous.

Solution

A conversational assistant wired into your docs, CRM, ERP and internal wiki. Sourced answers (never invented), human escalation when confidence drops below a threshold, fine-grained permissions per user profile. Deploys as a web widget, Slack, Teams or API.

Outcome

Tier-1 support reduction of 30-60%. New hire onboarding cut in half. Average response time under 3 seconds, vs 4-12 hours in human support.

Document analysis & extraction

Invoices, contracts, CVs, business PDFs — structuring chaos.

Problem

Your admin team manually retypes data from hundreds of invoices, contracts or CVs per month. Data entry errors are costly to reconcile. Processing delays block your cash flow.

Solution

Structured extraction pipeline: OCR (Mistral OCR, Vision GPT-4o), document classification, schema-validated JSON extraction (Pydantic), human-review dashboard for ambiguous cases. Webhook to your ERP or CRM for full automation.

Outcome

10-50× faster processing. Error rate cut by 5×. Typical savings: 1 FTE freed from admin tasks, reassigned to value-add work.

Semantic search (RAG)

Find a needle in an ocean of documents.

Problem

Your teams waste hours hunting for information in wikis, drives, Confluence, mail archives. Keyword search finds nothing when terminology differs. Internal knowledge sits underused.

Solution

Multi-source vector indexing (Confluence, Notion, Google Drive, mails, PDFs). Natural-language search with reranking, sourced citations, smart deduplication. Dedicated interface or Slack/Teams integration. Permissions inherited from your existing systems.

Outcome

Information search time divided by 8. Internal adoption of 70-90% at 3 months. Search precision (NDCG@10) typically jumps from 0.35 to 0.85.

Assisted generation (content, code, design)

Multiply creative and tech team productivity.

Problem

Your marketing, tech or design teams repeat low-value tasks: briefs, copywriting, prototypes, boilerplate code, design variants. Time isn't spent on innovation.

Solution

Internal generation tools: brand-aligned copywriting (fine-tuned on your voice), contextual code completion (Cursor, custom Copilot), image generation in your visual style (Midjourney, FLUX, custom ComfyUI). Editorial guardrails, human validation.

Outcome

Creative productivity ×2 to ×4 on repetitive tasks. Brand consistency reinforced (zero drift). Content time-to-market cut by 3×.

Autonomous agents

Multi-step workflows that run themselves.

Problem

Complex labour-intensive workflows: competitive intelligence, lead qualification, ticket follow-up, database updates. Too repetitive for humans, too variable for classic scripts.

Solution

AI agents with long-term memory and dynamic action plans. Each agent has tools (function calling to your APIs, web search, code exec, DB access) and guardrails (human validation on critical actions, audit logs, rollback). LangGraph or Claude Agent SDK architecture as needed.

Outcome

Automation of previously manual processes. Marginal cost divided by 10. 24/7 availability, no fatigue, no inattention.

Computer vision & media analysis

When the image speaks louder than text.

Problem

You have heavy volumes of photos, videos or scanned documents to process: quality control, moderation, classification, indexing. Too much for humans, too variable for fixed rules.

Solution

Vision-language models (GPT-4o, Claude Opus Vision, Gemini Pro Vision) or specialised models (custom YOLO, Anything segmentation). Inference pipeline with human-eval feedback loops for continuous fine-tuning. Edge deployment if latency-critical.

Outcome

Processing millions of images per day. Typical 92-98% precision depending on task. Inference cost optimised via batching and semantic cache.

How we work

Four steps, from brief to deployment.

No six-month big-bang. We start with one priority use case, prove value in 2-4 weeks, industrialise once ROI is proven.

Discovery & scoping (1 week)

2-hour workshop to identify the highest-impact use case. We look together at available data, constraints (GDPR, sovereignty, budget), target KPIs. We write a scoping document: scope, target architecture, estimated API costs, planning.

→ Written scoping + target architecture + API budget estimate

Testable MVP (2-4 weeks)

We build a functional prototype usable by your teams. We pick the relevant model (often GPT-5-mini or Claude Sonnet to start — controlled costs). Data pipeline, guardrails, minimal but real-condition usable interface.

→ MVP deployed on staging + 10 validated test cases

Industrialisation (4-8 weeks)

Once value is proven, we harden: scale-up, complete observability (Langfuse), continuous evaluation (eval sets, golden questions), API budget monitoring, fallbacks. We document for team handover.

→ Stable production + dashboards + docs + tech handover

Continuous evolution (monthly)

AI moves fast: new models every 2-3 months, prices drop, contexts grow. We stay actively watching to propose optimisations that make sense. Quarterly audit to decide on evolutions.

→ Quarterly review + recommendations + rolling roadmap

Models

The right model for the right problem.

Neither pro-OpenAI nor anti-Anthropic. We choose based on task: quality, latency, cost, sovereignty, context window. Here's how we reason.

OpenAI

GPT-5, GPT-5-mini, GPT-4o, o4

Strength

Creative generation, broad adoption, mature ecosystem (Assistants API, easy fine-tuning). o4 for complex reasoning.

Best for

Consumer chatbots, creative content generation, agents with complex function calling.

Pricing

GPT-5: $1.25/M in · $10/M out — GPT-5-mini 10× cheaper

Anthropic

Claude Opus 4.8, Claude Sonnet 4.6, Claude Mythos

Strength

Reference for code, long analysis (1M tokens), reliable agents. Built-in AI safety. No-training-default on data.

Best for

Code agents, complex document analysis, mission-critical tasks needing max reliability.

Pricing

Opus: $15/M in · $75/M out — Sonnet 10× cheaper, almost as good

Google

Gemini 3 Pro, Gemini Flash, Gemini Nano

Strength

Native multi-modal (text+image+video+audio in same request). Google Workspace integration. Gemini Nano embedded on Android and Chrome.

Best for

Video/audio analysis, Workspace-integrated projects, on-device with Nano, native web-augmented search.

Pricing

Gemini 3 Pro: $1.25/M in · $5/M out — often best quality/price ratio

Open-source

Llama 4 Maverick, Mistral Large 2, Qwen 3, DeepSeek V3

Strength

Self-hosted (EU sovereignty guaranteed). Free fine-tuning on your data. Marginal cost close to zero at scale. Performance very close to commercial frontiers.

Best for

Sensitive data (health, finance, defence), massive volumes where API cost explodes, deep business fine-tuning.

Pricing

Inference: from $0.20/M tokens on Replicate/Together — vs $1-15/M closed API

Integration patterns

The architectures we deploy.

Four patterns cover 95% of production AI projects. We combine based on your need, budget and sovereignty constraints.

pattern_1

RAG (Retrieval Augmented Generation)

Vector indexing of your documentation + LLM with mandatory citations. The answer cites your own sources, never invented. Full pipeline: smart chunking, embeddings (OpenAI text-embedding-3 or BGE for self-hosted), vector DB (Pinecone, Weaviate, pgvector or Qdrant based on your stack), reranking for precision.

When to use

When you have a documentation base (wiki, support, legal, technical) and answers must be factual and sourced. 80% of our projects.

pattern_2

Fine-tuning & dedicated embeddings

When prompt + RAG aren't enough: fine-tuning on your own data to teach a style, jargon or specific format. Possible on OpenAI (GPT-4o, GPT-5), Anthropic (Claude via API), Vertex AI, or open-source models (LoRA on Llama, Mistral). Business embeddings also possible for very high precision.

When to use

Highly specialised domain (legal, medical, niche tech). API token volume that explodes, making fine-tune cost-effective. Need for a business style or jargon no prompt can capture.

pattern_3

Function calling & tool use

The LLM calls your APIs, executes code, queries your DB. This turns a chatbot into a productive assistant. Architecture: you declare functions to the model (OpenAPI signature or JSON Schema), the LLM decides when to call them, your server executes and returns the result. Optional human validation on critical actions.

When to use

When users want to act, not just inquire: book, buy, update, create a ticket. Essential to transform an informative chatbot into a productive tool.

pattern_4

Multi-step agents

Autonomous task planning, long-term memory, human guardrails. The agent decomposes a goal into sub-tasks, executes them, handles errors, self-corrects. LangGraph (most mature), Claude Agent SDK, OpenAI Assistants or custom implementation based on complexity. Observability essential.

When to use

Complex multi-step tasks: competitive research, lead qualification, guided debugging, code refactoring. When a rigid workflow isn't enough and adaptation is needed.

Tech stack

The tools we actually use.

Proven stack on production deployments. No toys, no 3-month-old fads.

Orchestration

LangChain · LlamaIndex · LangGraph · Vercel AI SDK

Vector databases

Pinecone · Weaviate · Qdrant · pgvector · Turbopuffer

Embeddings & rerankers

OpenAI text-embedding-3 · Cohere · Voyage AI · BGE

Observability & evals

Langfuse · LangSmith · Helicone · Braintrust

Inference & hosting

Replicate · Modal · Together AI · Anyscale · Bedrock · Vertex

Security & guardrails

Lakera Guard · Rebuff · PII detection · NeMo Guardrails

定价

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