Custom AI agent development agency

Claude Code, Python/TS or self-hosted sovereign LLM.

Uclic builds custom AI agents: systems that execute real tasks in your operations, plugged into your tools and data, with the supervision that goes with them. From framing to production, choosing the technology according to your sovereignty and volume constraints.

4-6 wks agent in production (Pilot tier)100% self-hosted LLM sovereignty (GDPR)1 week to frame use cases and architecture
WladimirAlexis
Free growth audit
With Wladimir & Alexis30 minGoogle Meet
  1. 1Time slot
  2. 2Your details
ANSSI
Santévet
Gusella Bakker
Le Monde
Agicap
CodinGame
Deepki
10 AI Agent sub-services

Custom AI agents: 10 services, from use case to production

From scoping to production deployment, each sub-service can be activated standalone or bundled into the Production plan. Tools explicitly named: Claude Code, Anthropic SDK, OpenAI, Mistral, Llama, Qwen, Ollama, vLLM, LangChain, LlamaIndex, Pydantic AI, Vercel AI SDK, Mastra, pgvector, Qdrant, Pinecone, Langfuse, Helicone.

Diagnostic

AI use case & ROI audit

We map your internal processes and identify the 1 to 3 highest-ROI use cases: internal assistant, lead scoring, ticket classification, product content generation, document RAG. Each case is quantified in hours/month saved, expected NPS, and CAC.

1-3prioritized use cases
12-month ROIPriority use casesHours/monthNPS
Alexis Christine-AmaraDedicated expert Alexis Christine-Amara VP Sales
RAG

RAG & vector database

Indexing of Notion, Confluence, Drive, S3 and PostgreSQL into pgvector (Supabase), Qdrant or Pinecone. Embeddings via OpenAI text-embedding-3-large or Mistral Embed. Reranking. Mandatory source citations in every answer: no silent hallucination.

100 %sourced answers
pgvectorQdrantPineconeReranking
Wladimir DelcrosDedicated expert Wladimir Delcros Head of Growth
Build Python

Python agents

Custom Python agents built for specific cases: automated lead scoring, support ticket classification, orchestrated multi-agent systems, business RAG. Strict Pydantic validation, deterministic fallback, FastAPI exposed internally.

4Python frameworks
LangChainLlamaIndexPydantic AIFastAPI
Mathieu ChenalDedicated expert Mathieu Chenal Expert SEO & GEO
Build TypeScript

TypeScript agents

For TS/Next.js teams: Vercel AI SDK for conversational agents embedded in your app, Mastra for stateful agents with complex workflows. Streaming, tool calling, multimodal. Deployment on Vercel or Fly.io.

100 %TS stack supported
Vercel AI SDKMastraNext.jsStreaming
Moussa BallDedicated expert Moussa Ball AI Architect & Full-Stack Engineer
Integration

Connection to your business tools

Connecting the agent to your production tools: HubSpot, Pipedrive, Salesforce, Stripe, Linear, Slack, Notion, your in-house API. Webhooks, OAuth, fine-grained error handling. No isolated POC: the agent lives in your stack.

20+connectable tools
HubSpotPipedriveSlackLinearIn-house API
Valentin GeffroyDedicated expert Valentin Geffroy Webflow Expert Certified | JS, GSAP & CRO
Observability

Cost and quality monitoring

Full traces of prompts, responses and tool calls in Langfuse or Helicone. Alerts on cost drift (token spend), abnormal latency, hallucination rate, tool call failure. Golden dataset of 50-200 cases to measure regression at every iteration.

8+metrics tracked
LangfuseHeliconeGolden datasetAlerts
Pierre LafontDedicated expert Pierre Lafont Full-Stack Vibe Coder & AI Engineer
Production

Deployment, docs & support

Production deployment with runbook, documentation for your teams, half-day training. 30-day warranty on the Production tier. 6-month support + fine-tuning on the Sovereign tier. You keep full ownership of the code, prompts and models.

6 monthssupport included (Sovereign)
RunbookDocumentationTraining6-month support
Dimitri GauthierotDedicated expert Dimitri Gauthierot Expert Paid Media | Google, Meta, LinkedIn & TikTok Ads (Founder DG Labs)
What we do differently

Your AI agents without / with Uclic

Move from a generic chatbot to custom agents, plugged into your stack and supervised.

Without Uclic

Generic chatbot

A bolted-on AI SaaS. Nothing is connected to your tools or your data.

×Generic chatbot
×No tools connected
×Made-up answers
×Undetected regressions
×No trace
×Data out of control
×Unpredictable AI budget
×Risky go-live
With Uclic

Custom agent

A deliberate architecture. An agent built for your stack and supervised in production.

uclicAI runtime
SENIOR TEAMYOUR CODEGDPROBSERVABILITY
An agent in production
From tool to actionEXPLORE THE SYSTEM

Model, tools and data work together: every action the agent takes is logged, evaluated and improved.

+

autonomy

The agent runs complex tasks end to end, within a defined scope.

+

security

Sensitive data handled to your constraints, sovereign LLM when needed.

+

traceability

Every call, tool and answer is logged.

+

cost control

Cost per call tracked and model matched to the task.

Our AI agent method

How we build a custom AI agent in 4 to 12 weeks

From use-case scoping to production deployment, 6 structured steps. Explicit architecture choice: Claude Code, custom Python/TS, or self-hosted LLM, depending on data sensitivity and volume.

  1. W11 week
    1 week· Step 1

    We identify 1 to 3 high-ROI use cases (internal assistant, lead scoring, ticket classification, content generation, document RAG). We quantify the expected savings: hours/month, NPS, CAC. We decide between Claude Code, custom Python/TS, or self-hosted LLMs based on data sensitivity and volume. If the ROI doesn't add up mathematically, we tell you before you sign.

    • 1 to 3 quantified use cases
    • Claude Code / Python / sovereign LLM selection
    • Documented target architecture
    • 12-month ROI estimate
    Deliverable· Architecture brief + 12-month ROI estimate
  2. 02
  3. 03
  4. 04
  5. 05
  6. 06
Pricing

Pricing: how much does a custom AI agent cost?

Three explicit tiers. From a 4-6 week pilot to a self-hosted sovereign agent with 6 months of support. Project-based commitment (fixed fee), not monthly: you know to the euro what you're paying.

Diagnostic

Free audit

No commitment
0€

A free first diagnostic, with the priority actions.

    Get my diagnostic
    Kickoff

    Pilot

    from
    Custom quote

    1 agent, 1 use case, 4-6 weeks. Demo + production-grade deliverable. To validate AI in your context before scaling.

    • 1 scoped use case + quantified ROIA half-day workshop to identify the priority use case and quantify the expected savings (hours/month, NPS, CAC).
    • 1 functional agent in productionBuilt with Claude Code SDK, or Python (LangChain/Pydantic AI) or TS (Vercel AI SDK/Mastra) depending on your needs.
    • RAG on 1 document sourcepgvector or Qdrant indexing of a limited corpus (Notion, Drive, PostgreSQL database). Sourced citations mandatory.
    • Basic Langfuse observabilityPrompt/response traces, cost dashboard, simple drift alerts.
    • Demo + handover + docsFinal demo, runbook, half-day training. No post-delivery support on this tier.
    Start with Pilot
    Most pickedScaling

    Production

    from
    Custom quote

    1 to 3 agents integrated into your stack, full monitoring, multi-source RAG, complete documentation. To go from POC to production.

    • 1 to 3 agents integrated with CRM / business APIsConnection to HubSpot, Pipedrive, Slack, Linear, your in-house API. Webhooks and OAuth handled.
    • Multi-source RAG (Notion + Drive + DB)pgvector/Qdrant/Pinecone indexing with reranking. Sourced citations mandatory.
    • Full Langfuse / Helicone observabilityTraces, cost/latency/hallucination alerts, golden dataset of 50-200 cases to measure regression.
    • 30-day post-delivery guaranteeFixes and adjustments included for 30 days after go-live.
    • Documentation + team trainingComplete runbook, user docs, 1-day training for your teams.
    • Your choice of Anthropic Claude / OpenAIProprietary model (Claude Sonnet/Opus 4 or GPT-4o/o1) depending on your use case and token budget.
    Start with Production
    GDPR-sensitive

    Sovereign

    from
    Custom quote

    Self-hosted LLM (Mistral, Llama, Qwen), security audit, fine-tuning if needed, 6 months of support. For data that must never leave your infrastructure.

    • Self-hosted open-source LLMMistral 7B/8x7B, Llama 3.3 70B or Qwen 2.5 deployed on Ollama, vLLM or dedicated GPU (RunPod, Scaleway, AWS Bedrock EU).
    • No data externalized100 % inference on your machine or server. No leakage to OpenAI, Anthropic or Google. Documented GDPR compliance.
    • Security audit + network isolationFull audit: VPC isolation, encryption at rest, granular access control, auditable logging.
    • Fine-tuning on your data (if relevant)LoRA/QLoRA fine-tuning on your internal corpora to specialize the model. Discussed in week 2 based on ROI.
    • 6 months of support includedModel maintenance, updates, prompt optimization, adding new use cases. Monthly steering committee.
    • Everything in ProductionMulti-source RAG, observability, CRM/business API integration, documentation, training.
    Start with Sovereign

    See the detail of all our plans or find out who we are.

    AI & Dev sub-services

    Your AI agents connected to your workflows, CRM and data

    Case studies

    Custom AI Agents engagements: every case comes with raw numbers, real context and what we actually did.

    Comparison · method published

    Comparing several AI agents agencies?

    Our ranking of French agencies, based on verified Google reviews and their actual specialty, with the method published.

    See the best AI agents agencies
    FAQ

    Custom AI agents FAQ : costs, timelines, data, production rollout

    What is a custom AI agent?
    A custom AI agent is a system built to execute one specific task in your business: qualifying inbound requests, enriching records, preparing responses, monitoring signals. Unlike a generic chatbot, it is connected to your tools and your data, follows your business rules, and operates under supervision. A good agent is measured by the time it gives back to your teams and the errors it prevents.
    Claude Code, custom Python, or self-hosted LLM: how do you choose?
    Three decision criteria. (1) Data sensitivity: if you handle legal, HR, medical, or financial data that cannot leave your environment, the self-hosted LLM (Mistral, Llama, Qwen on Ollama/vLLM) is the way to go. (2) Code-aware use cases: PR review, test generation, refactoring: Claude Code SDK is unbeatable, with delivery in days rather than weeks. (3) Specific business cases (complex lead scoring, orchestrated multi-agent, vertical business RAG): custom Python with LangChain, LlamaIndex, or Pydantic AI. We make the call with you in W1, ROI in hand: no dogma.
    Why a sovereign LLM hosted with us, rather than OpenAI or Anthropic?
    Three concrete reasons in 2026. (1) GDPR & professional secrecy: lawyers, doctors, HR, banking: your data cannot transit through a US server, even with a signed DPA. The CNIL is explicit on this. (2) Cost at scale: beyond 50M tokens/month, a Mistral 7B or Llama 3.3 on dedicated GPU (RunPod ~quote-based) costs less than OpenAI. (3) Latency & availability: a local LLM responds in 200-500ms with no rate limit and no external API outages. Mistral/Llama 3.3 70B / Qwen 2.5 performance = 85-95% of GPT-4o on most business tasks (measured on MMLU, HumanEval, MT-Bench).
    How long before an AI agent is in production?
    Standard rollout plan. Pilot (quote-based): 4-6 weeks, 1 agent + 1 use case. Production (quote-based): 8-10 weeks, 1 to 3 integrated agents + multi-source RAG + monitoring. Sovereign (quote-based): 10-12 weeks, self-hosted LLM + security audit + 6 months of support. No POC that drags on for 6 months: every phase has a measurable deliverable. If the agent doesn't deliver the ROI by W6, we pivot or refund the gap: it's written into the contract.
    Which use cases deliver the best ROI for AI agents?
    Five use cases proven with our SME and mid-market clients. (1) Internal legal assistant (RAG over contracts + case law): 60% time saved per lawyer. (2) Automatic lead scoring (buying signals + enrichment): +35% SQL conversion rate. (3) Support ticket classification: -45% routing time, +12 NPS points. (4) Product page generation (e-commerce, catalogs): 10x publishing cadence. (5) RAG over internal knowledge base (Notion + Confluence + Drive): 70%+ adoption within 3 months across ops teams.
    How do you prevent hallucinations in production?
    Five mandatory safeguards. (1) RAG with sourced citations: no answer without a link to the source in Notion/Confluence/DB. (2) Strict Pydantic validation on structured output: the agent never returns malformed JSON. (3) Deterministic fallback: if the confidence score is too low, we fall back to a business rule or human escalation. (4) Golden dataset of 50-200 cases with expected output, replayed on every prompt change: regression detection. (5) Human oversight on critical actions (sending client emails, CRM updates, contract approval): the agent proposes, the human validates. Residual hallucination rate < 2% in production.
    Which models do you actually use, and why?
    No black box: explicit choice per use case. Claude Sonnet 4 / Opus 4 (Anthropic): long-form reasoning, code-aware agents via Claude Code, high-quality French writing. GPT-4o / o1 (OpenAI): complex reasoning, multi-modal, mature ecosystem. Mistral 7B / 8x7B: French open source, excellent performance-to-cost ratio when self-hosted. Llama 3.3 70B (Meta): open source, performance close to GPT-4o, runs on a single H100 GPU. Qwen 2.5 (Alibaba): open source, strong code and multilingual performance. Embeddings: OpenAI text-embedding-3-large or Mistral Embed depending on sovereignty requirements.
    Which Python and TypeScript frameworks do you use?
    Python: LangChain for mature multi-step workflows, LlamaIndex for high-precision RAG, Pydantic AI for strict validation of structured outputs (our default choice on new projects), FastAPI to expose the agent as an internal API. TypeScript: Vercel AI SDK for conversational agents integrated into Next.js with native streaming, Mastra for stateful agents with complex workflows (the TS equivalent of LangChain). We choose in W1 based on your existing stack: no forced rewrites.

    Got a specific question? We answer it during the free audit.

    Wladimir DelcrosBy Wladimir Delcros, Head of Growth · Updated on October 29, 2026
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    Senior leadership, certified media buyers, AI agents in production. No interns on your account: the team you meet is the team that ships.

    Lourmarin offsite · September 2025Meet the whole team Apply to uclic
    The Uclic team | three rows of smiling colleagues, arms raised, wearing green uclic t-shirts