AI Implementation
AI that earns its keep. In your business, not as a demo.
Practical AI deployed inside the systems your team already uses. Lead scoring on your CRM data. Demand forecasting on your inventory. Document extraction from supplier invoices. Customer-service triage. Copy drafted against your brand guidelines. Built on your data, deployed where work happens (Odoo, browser, email), measurable ROI inside 90 days.
Right for you if
- You have a specific business problem, not "we should use AI somewhere".
- You have data the AI can train against or be anchored to.
- You want it deployed inside your existing tools, not as another standalone SaaS the team has to log into.
Not right for you if
- You want a public-facing chatbot for the marketing site. Different scope, ask separately.
- You want to train a new foundation model from scratch. We compose with Anthropic, OpenAI, and open-weight models; we do not pre-train.
- You want AI to replace headcount on day one. We deliver augmentation that pays back in months; the headcount question is yours to make later.
Methodology
How a AI Implementation engagement actually runs.
5 phases. Every artefact written down, every decision logged, every handover documented.
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01
Use-case definition
Pick one business problem with measurable outcome. Lead scoring with conversion uplift target, invoice extraction with error-rate target, forecasting with MAPE target. We rule out the demos that sound impressive but never ship.
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02
Data assessment
Audit the data the model needs: volume, quality, labels, freshness. Honest gap analysis: build now, or fix the data first. We have walked away from engagements at this gate when the data was not ready, and would again.
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03
Proof of concept
A working model on a held-out slice of your real data. Reported with accuracy, precision, recall, and a confusion matrix you can read. You see whether it works before we build the integration.
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04
Integration
Deploy where the team works. Inside Odoo as a custom field or wizard, in the browser as a side-panel, in email as a draft generator, or as an API behind your existing tools. The user does not learn a new product.
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05
Measure and iterate
Production monitoring on accuracy and business KPI together. Retraining cadence agreed up front. ROI report every 30 days for the first quarter so you see the system pay for itself.
What you receive
Deliverables.
Every artefact handed over to you. Code, configuration, documentation, training material. Yours to keep, yours to share with any successor.
- Use-case scope and success metric, signed off before build starts
- Proof-of-concept model with held-out validation
- Production deployment inside the system your team uses
- Monitoring on accuracy, latency, cost, and business KPI
- Retraining pipeline, scheduled or triggered by drift
- Operator runbook so your team can run the model without us
- 30-60-90 day ROI report
Frequently asked
Questions about AI Implementation.
Which models do you use? Anthropic, OpenAI, open-weights? +
Whichever fits the problem and your data-residency rules. Anthropic Claude for reasoning, document understanding, and generation. OpenAI for similar plus image. Open-weight models (Llama, Qwen, Mistral) when data must never leave your infrastructure or when fine-tuning is the right call. We pick after the use-case is defined, not before.
Does our data train the model vendor’s next foundation model? +
No. We use the no-training tiers from Anthropic and OpenAI by default (their enterprise / API plans exclude training on customer data). For open-weight models we run inference on infrastructure you choose; the data never leaves it. We document the data flow before any deployment.
What about hallucinations? Can we trust the output? +
Every deployment is anchored: retrieval-augmented against your real data, structured-output schemas the model must conform to, and a confidence threshold below which the output gets queued for human review instead of acted on. We measure hallucination rate explicitly and report it monthly.
Can you integrate AI directly into Odoo? +
Yes, that is the most common pattern. Custom fields populated by AI on lead create, invoice extraction wizards in the supplier portal, AI-suggested replies inside the helpdesk, all native Odoo UI. The user works in Odoo and the model is invisible.
How do you handle PII and consent? +
PII is masked or tokenised before it leaves your environment, where the regulations require. Consent flags from the source system are honoured downstream. We sign a DPA, route through your DPIA, and document data lineage so the audit trail is complete.
What does success look like in 90 days? +
A signed-off scope hitting its accuracy target, deployed in production, in daily use, with a measurable business KPI movement and an ROI calc that pays back the engagement inside the first year. If we cannot see that path at use-case definition we will tell you and not start.
Other services
Pair AI Implementation with one of these.
Tell us about your AI Implementation project.
A short note is enough. We answer in person, within one business day.