AI Systems That Reach Production, Not Just Demo
RAG assistants, private LLM deployment, and autonomous agents — engineered for your data, your stack, and a running cost you approved in advance.
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Demo vs Production
The gap between an AI demo and an AI system in production is where almost every project dies.
A demo answers ten questions well. A production system answers ten thousand, including the badly worded ones, the ones about products you discontinued, and the ones designed to make it say something you will have to apologise for. It needs retrieval that surfaces the right document rather than a plausible one. It needs citations, so a wrong answer is traceable instead of mysterious. It needs a cost model, because token spend scales with success and a popular assistant can quietly become your third-largest software bill.
It also needs an honest answer to the question nobody asks first: does this need to be AI at all? A well-written FAQ page and a search index solve a surprising number of the problems that get scoped as chatbots.
We build the ones that genuinely need building.
What We Build
RAG Assistants & Support Agents
- Retrieval-augmented generation grounded in your catalogue, docs, and policies
- Source-cited answers so every response is auditable
- WooCommerce product and order awareness
- Multilingual: English, French, Arabic
- Human handoff and escalation logic
- Embeddable widget or full API
Private & Local LLM Deployment
For data that cannot touch a third-party API.
- Ollama, LM Studio, and llama.cpp deployment
- Open models: Llama, Mistral, Qwen, DeepSeek, Phi
- GPU sizing with a cost-versus-cloud comparison before you commit
- On-premise or your private cloud
- Data never leaves your infrastructure
AI Agents & Automation
- Tool-using agents for multi-step tasks
- n8n and Make.com AI-node orchestration
- Email triage and response drafting
- Lead qualification and CRM enrichment
- Guardrails and spend ceilings on every agent
Content & Catalogue Pipelines
- Bulk product description generation with brand-voice control
- Multilingual catalogue translation
- SEO metadata and schema automation
- Human review and approval gates before anything publishes
Semantic Search
- Vector databases: pgvector, Pinecone, Chroma
- Ingestion and chunking pipelines tuned to your document types
- On-site product search that understands intent
- Internal team knowledge bots
Strategy & Cost Engineering
- Model selection across cloud and local
- Token-cost forecasting at projected volume
- Prompt engineering and system design
- Caching and routing to cut API spend
- Honest build-versus-buy advice, including "do not build this"
Common Mistakes We Find
Patterns from audits we have run. If you recognise two or more, the gap is probably costing you more than it appears to.
No retrieval grounding.
The model answers from training data instead of your catalogue, confidently describing products you discontinued and policies you never had.
No token spend ceiling.
Cost scales with usage and there is no cap. A popular assistant becomes a significant monthly bill with no alerting until it is a line item someone questions.
Chunking that ignores document structure.
Retrieval splits a spec table across two chunks, so the model receives half a table and answers as though it received all of it.
No escalation path.
The assistant cannot say "I do not know" and hand off, so its worst answers go to the customers with the least common questions.
Customer data sent to a third-party API with no DPA in place.
A GDPR problem introduced by a feature nobody classified as data processing.
How We Work
Step 01 — Scope & ROI
We identify the highest-value opportunity and define a success metric before writing code.
Step 02 — Model & Cost Plan
Right model, cloud or local, with a running-cost forecast you sign off.
Step 03 — Build & Integrate
Pipeline engineering, stack integration, guardrails against hallucination and runaway spend.
Step 04 — Evaluate
Real-world testing against the success metric, with prompt tuning until it performs.
Step 05 — Deploy & Monitor
Production rollout with logging, cost monitoring, and ongoing support.
What Happens Next
Step 01 — You hear back within 24 hours.
A real reply from the person who would do the work, not an autoresponder or a junior scheduling a call about a call.
Step 02 — We look before we talk.
Send us access or a URL and we review your actual setup first, so the conversation starts with findings instead of discovery questions.
Step 03 — 30 minutes, findings first.
We walk you through what we found and what it is costing. You get that regardless of whether you hire us.
Step 04 — A written scope, or an honest no.
If it is a fit, you get scope, timeline, and cost in writing. If it is not, we say so and point you somewhere better.
No retainer required. No minimum term. No obligation at any step.
Is This Right For You?
We would rather tell you now than after an invoice. Here is who this work pays off for, and who it does not.
A good fit if:
- You are doing €25,000+ per month in online revenue, where a few percent of recovered attribution is real money
- You are running paid media and the reported numbers do not match your bank
- You sell across more than one market, currency, or storefront
- You have a developer or agency who can act on what we find
- You want to own the implementation afterwards, not rent it
Probably not a fit if:
- You are early stage and validating the product — fix demand first, measure it later
- You want someone to manage ad spend day to day; we build the measurement layer, we are not a media buying agency
- You need it live this week; proper implementation has a validation phase and we will not skip it
- You want the cheapest quote — we are not it, and the cheapest tracking build usually gets rebuilt
Most engagements start from €1,500. We confirm scope and cost in the discovery call, before anything is committed.
Tell us about your setup. We respond within 24 hours.
Not ready to write it all out? Book a 15-minute discovery call →
Common
Questions
Retrieval-augmented generation grounds every answer in your actual documents, and we require source citations so an unsupported claim is visible rather than invisible. Where the system cannot find a grounded answer, it says so and escalates instead of guessing.
Local wins on data privacy, predictable cost at high volume, and no vendor lock-in, at the price of hardware and maintenance. Cloud wins on capability and zero infrastructure. We model both against your volume and requirements and give you the numbers rather than a preference.
That is the question most AI projects skip until it is a problem. We forecast token spend at projected volume during scoping, then engineer caching and model routing to reduce it. You approve a monthly figure before we build.
Yes, through authenticated API integration with your store — with scoped permissions and access controls so it can only see what a given user is entitled to see.
Self-hosting removes the third-party transfer problem, which is a large part of it. You still need a lawful basis, retention limits, and appropriate security. We build to what your counsel specifies and document the data flow.
Build cost starts around €1,500 for a focused implementation and scales with data pipeline complexity. The number that matters more is the running cost, which most projects discover too late — we forecast token spend at your projected volume during scoping and engineer caching and model routing to control it. You approve a monthly figure before we build.
Typically three to six weeks. Deploying a model against your documents is fast. The time goes into ingestion and chunking tuned to your document types, evaluation against real questions, and the guardrails that stop it answering confidently when it should escalate.
Cloud models are more capable and require no infrastructure. Self-hosted models keep data on your systems, cost predictably at high volume, and avoid vendor lock-in. For customer-facing assistants handling ordinary catalogue questions, cloud usually wins. For anything touching data that cannot leave your infrastructure, self-hosted wins regardless of capability. We model both against your volume.
No, and treating it that way is how these projects fail. A well-built assistant handles the repetitive majority — order status, product fitment, policy questions — and escalates everything else with context attached. Your team spends less time on the questions that never needed a human. The ones that did still reach one.
Not with the configurations we deploy. Business tiers of the major providers exclude API inputs from training by default, and self-hosted models never transmit data at all. We document the exact data flow for each integration so your privacy policy and any DPA request can be answered from a written record rather than an assumption.
Ready to Build Something That Works?
Book a free 30-minute strategy audit. No pitch deck, no pressure — just an honest look at your setup and what to fix first.
Not ready to write it all out? Book a 15-minute discovery call →
