AI proof of concept development services, fixed in scope and price.

An AI pilot covers one workflow, end to end. That might be qualifying inbound leads, triaging support tickets or extracting data from invoices. We start with discovery: how the work happens today, which systems hold the data and where people spend their time. Then we build one agent with its core integrations and deploy it in your cloud, so it runs against your real data, not a demo dataset.

Keeping the scope to one workflow is deliberate. It keeps the price fixed, the timeline tight and the result easy to judge. Anything that comes up outside the agreed scope goes on the roadmap instead of quietly growing the pilot, so you always know what you are paying for.

• Discovery and agreed success metrics
• One agent with its core integrations
• Deployed in your cloud, on your real data
• Evaluation report and roadmap

A generative AI proof of concept tests whether a model can find or produce the right answer from your data. Examples: a knowledge assistant that searches your documents, CRM and wikis and cites its sources; a support assistant that drafts answers from your knowledge base; or a content copilot that writes drafts in your brand voice for your team to edit.

An agentic AI proof of concept tests whether software can finish multi-step work across your tools, with human approval where decisions matter. Examples: a sales agent that qualifies leads and books meetings, an operations agent that processes documents and routes exceptions, or a finance assistant that extracts invoice data and flags mismatches for reconciliation.

• Real estate: qualify portal and WhatsApp leads, book viewings
• Logistics: document AI for invoices and bills of lading
• Clinics: voice agents for appointments and reminders
• Professional services: knowledge assistant over firm documents
• E-commerce: shopping assistant and product search

Before we build, we agree what success means for your business, in measures you already track or can measure during the pilot. Typical measures include answer accuracy on a test set drawn from your data, time saved per task, the share of cases handled without escalation, response time and cost per task. We set the threshold for each one with you up front, so the result isn't open to interpretation.

At the end you get an evaluation report: how the system performed against each metric, where it failed and why, what running it would cost and what it would take to reach production. It closes with a clear recommendation to go, change course or stop. A no-go is a useful result too: you learn it for the cost of one pilot, not a full build.

If the answer is go, the pilot is not thrown away. It already runs in your cloud on your data, with an evaluation suite you can rerun before every release. Moving to production usually means hardening: more integrations, monitoring and tracing, human-in-the-loop approvals where they matter, cost controls, and handling the edge cases the pilot surfaced.

You can take that work forward as a milestone-based Product Build or a Dedicated AI Pod on a monthly retainer. Either way, the same senior people who scoped and built the pilot carry it into production, and you own 100% of the code and IP throughout. The roadmap from the report becomes the plan for the next phase.

We don't publish a price list, because cost depends on the workflow. The main drivers are the number of systems to integrate, the state of your data, how many languages or channels are involved, and how much evaluation the use case needs. After the scoping call, you get a fixed proposal with deliverables, cost and timeline. Nothing starts until you agree to it.

Once agreed, the price does not move. If we find something mid-pilot that would change the scope, we raise it with you, and you decide whether to add it, swap it for something else or leave it for the roadmap. You see a working system on your real data early, with a live demo every week until the report.