AI services
AI-Native Product Development
A product designed around the model from day one, with the platform engineering to run it: multi-tenancy, queues, observability and cost control.
At a glance
- From idea to working prototype in weeks
- Agent, API, web app and data pipelines from one team
- Multi-tenant, observable and cost-controlled from the start
- Handover to your team, or ongoing operation
An AI-native product is not an existing app with a chat box. The model is in the critical path, so latency, cost, grounding and failure modes shape the product from the first screen. We have built three of them since 2024, and the platforms that run them.
Who it's for
- Founders with an AI product idea and a deadline
- Companies productising an internal AI capability for their customers
- Teams whose prototype worked and now needs to become a product
What we build
- The agent or model layer, designed as described under AI agents and RAG.
- The product around it. Web and mobile-first apps in Blazor, SvelteKit or React, in the languages your users speak, with onboarding, accounts, billing and admin.
- The data layer. ETL pipelines, a modelled database, and the integrations the product depends on, with queues between every stage.
- The platform. Multi-tenancy, identity, rate limits, observability with OpenTelemetry, per-tenant cost accounting, and the evaluation harness that keeps quality from drifting as models change.
How it's done
A small senior team, usually the founder plus one or two engineers, working in short cycles against a real user flow. The first milestone is always a working prototype on real data, because that is where an AI product's real risks show up. From there we harden what proved valuable and cut what did not.
Proof
MayAI: a consumer agent in English and Arabic, from a chat message to restaurants, menus, a cart and checkout, built to a working prototype by an architect and one developer. Valco AI: data pipelines over public property records, forecasting models, conversational recommendations and a dashboard, delivered to the client. DevGuardian AI: a multi-agent platform we built as a product of our own and licensed.
Engagement shape
Discovery, a prototype milestone, then production in fixed-scope increments. We can hand the product to your team or keep operating it.
Frequently asked questions
Which stack do you use?
.NET and Blazor or SvelteKit on the product side, Python where the models live, Azure most often, with Microsoft Agent Framework, Azure OpenAI and pgvector or Azure AI Search underneath. We pick for your team's ability to maintain it, not for novelty.
How fast can we see something real?
A working prototype on real data within two to six weeks of starting, depending on integrations. MayAI went from a chat message to a working order flow in that window.
Can you take over an existing prototype?
Yes. We assess it first, keep what holds up, and replace what will not survive production, which is usually grounding, data handling and observability.
Related services
AI Agents & Multi-Agent Systems
Role-based agents that act on real systems: tool calling, memory, structured replies and human approval where it matters. Built on Microsoft Agent Framework, LangChain and OpenAI-compatible models.
MCP Servers & AI Integrations
Put your product or data in front of ChatGPT, Claude and other AI hosts through the Model Context Protocol. Secured with OAuth 2.1 and tested against each host's rules.
RAG & Knowledge Systems
Retrieval built for your material, not generic chunking: hybrid search, GraphRAG, code-aware indexing and incremental refresh on pgvector, Azure AI Search or Neo4j.
Talk to the people who would build it
Tell us what you are trying to do with ai-native product development. We will come back with an honest take and a plan.
Book an AI consultation