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NovaTech AI Delivery
Connecting it is only the beginning. What decides whether it keeps paying off is whether the scenario was well chosen, whether metric definitions line up, how permission boundaries are drawn, and who looks after it once it's live. NovaTech works in delivery units — one scenario at a time, moving into ongoing managed service after go-live. Each stage has a clear scope, defined deliverables, and one metric you can sign off against together.
Connecting AI, getting it running, seeing it work — most enterprises have done that part. The stretch after it is where fewer have found their footing: Gartner reports that roughly 89% of agent pilots don't make it into everyday production use. It usually comes down to three things.
Whether a scenario survives long term is often decided before it starts: whose daily work it serves, and which metric improves once it works. Half a day spent on that question early tends to shape the outcome more than the three months of development that follow.
Gartner expects around 60% of AI projects to slow down through 2026 because the data isn't ready. The usual issue isn't connectivity — it's the same metric being calculated differently in different departments. AI will pick one method without saying which, and colleagues naturally hesitate to act on a number when they can't tell.
Models drift, processes change, and cost moves with usage. Monitoring, prompt tuning, permission adjustments, cost tracking — none of it is complicated, but it needs a standing owner. Most pilots stop here, usually not because the work was poor, but because it was only ever scoped as a one-off project.
Every delivery unit has a clear scope and defined deliverables, and moves into managed service once complete. What the previous unit leaves behind is directly reusable by the next scenario.
Start from everyday workflows and look at which scenario genuinely moves a metric. Assess data conditions and confirm owners, then settle on a success standard everyone recognises.
Scope, deliverables and price are agreed before work starts. One scenario, taken to production-ready, signed off, and handed over cleanly to your team.
Go-live isn't the finish line. Monthly managed service: outcome monitoring, model and prompt tuning, token and capacity cost management, quarterly expansion reviews.
The data foundation, metric dictionary, security baseline, identity and permission model and agent scaffolding all stay with you as long-term assets, ready for the next scenario.
Once the foundation, metric definitions and security baseline are in place, the next scenario starts from there rather than from scratch
Above is how units connect to each other; here are the six stages inside one. Each has a defined output, so both sides can check progress at any point.
Map workflows by department, rank by KPI, and assess data reachability and technical feasibility.
Get trustworthy data within reach of the AI: unified definitions, semantic layer, retrieval and permission boundaries.
Pick the path that fits the scenario: low-code agent, pro-code agent, or existing Copilot capability.
Content safety, agent identity, row-level permissions, auditing and compliance — advanced alongside the build.
Replicate from one scenario to several, with a unified model gateway and call routing to limit duplicated effort.
Observability, cost management, outcome regression, and iteration as the business changes.
"Your IQ" produces nothing you can demo, yet it determines how far the other five stages can be trusted — we usually suggest leaving it real time
These five aren't five things you buy separately — they carry one another: data underneath, the agent platform in the middle, the surfaces people work on above. Security and delivery run vertically through all three, rather than being added at the end.
One team, from choosing the scenario through to running it
Work IQ carries the signals of how an organisation actually runs — meetings, mail, documents, working relationships. It's what lets an agent understand who does what.
Foundry IQ is the unified knowledge layer for agents — permission-aware retrieval with automatic source routing, so every agent gets context that is both correct and within what it is allowed to see.
Fabric IQ gives business data meaning a machine can read — entities, relationships, business rules. Define it once and every agent above shares the same definitions, which is what makes their answers reconcile.
Built through all three layers in step, not bolted on before go-live
Reading upward: once data carries meaning, agents can rely on it; once agents carry identity and permissions, the surfaces can be opened to everyone
A delivery unit is made up of four roles, mixed according to what the scenario needs rather than a fixed headcount.
Accountable for the metric, not for hours booked. Scope, pace and acceptance criteria close with them, and there is one person to go to.
The people writing the code talk to the people doing the work directly, rather than through a requirements document. One fewer relay, one fewer chance to drift.
Makes data reachable, aligns definitions, builds the semantic layer. This is the role that decides whether the AI can be trusted.
Permission boundaries, content safety, audit and compliance — involved alongside the build rather than appearing at the go-live review.
Microsoft 365 Copilot, Copilot Studio, GitHub Copilot, Microsoft Foundry and Microsoft Fabric, through to ongoing operation after go-live, are all handled by the same team. One point of contact from choosing the scenario to running it, with none of the coordination falling to you.
Two projects with different starting points: one began at the agent platform, the other at the data foundation.
A multi-agent platform built on Microsoft Agent Framework and deployed on Microsoft Foundry. Three design decisions carry the architecture: every agent holds its own Entra Agent ID, the full call chain is traceable through OpenTelemetry, and idle agents scale to zero to keep cost in hand.
Wind and solar plant operating data, together with ERP finance, CRM customers and years of historical Excel, brought onto one foundation in Fabric and OneLake. The approach was to write the enterprise metric dictionary and settle cross-department definitions first, then start development — so reports and agents afterwards share one set of definitions.
NovaTech is a Microsoft Solutions Partner working across the Microsoft stack since 2011, serving more than 1,000 enterprises from strategy through to ongoing operation.
All six Microsoft Cloud Solution Partner designations:
Advanced Specializations and dual-channel credentials:
A: One unit covers one business scenario, and the scope is written down at the assessment stage: which data sources and systems are involved, which roles will use it, and which metric defines success. Deliverables include the production-ready application or agent, the supporting data and permission configuration, the security baseline, and documentation your team can pick up from. The timeline depends on source conditions and integration complexity, and is stated in the assessment conclusion rather than promised as a number up front. Requests outside the scope are noted and planned into the next unit, so both sides stay clear on what's in flight.
A: It includes outcome monitoring and alerting, model and prompt tuning, token and capacity cost tracking, minor adjustments, and a quarterly expansion review. It does not include development of new scenarios — that belongs to the next delivery unit, and we look at whether it's worth doing together at the quarterly review. Azure and Microsoft licence costs are paid by you directly and sit outside the managed service fee; we recommend listing them separately in the quote so they're easy to take through your own budget process.
A: Not long-term on site, but always present at the moments that matter — scenario assessment, workflow observation, integration testing, go-live and training all happen in person. Day-to-day development and iteration run remotely, with progress and output synced weekly. The advantage of working this way is that our effort goes into building up methods and reusable assets that make your later scenarios faster, rather than into hours spent on your premises.
A: Yes, provided the order is right. In most first units, a fair share of the effort goes into connecting data, settling definitions and drawing permission boundaries. None of it produces something you can demo, but it determines how far the results can be trusted. We usually suggest picking a first scenario with reasonably good data conditions, getting the foundation and the method running smoothly, and taking on the harder parts after that.
A: That is the first thing we confirm — where your tenant sits, which entity signs, whether data can leave the region, and whether there are overseas branches. The same business requirement leads to different workable architectures for a mainland-only entity and for a company with an overseas entity. NovaTech holds 21V CSP credentials alongside Hong Kong and Singapore CSP, so either side is workable. We settle this at the assessment stage before putting forward a solution and a price.
A: We deliberately leave two exits. The first is the model layer: NovaHub Gateway provides a single entry point, so switching models doesn't change business code. The second is the delivery standard: every unit's acceptance criteria include your team being able to take it on and keep changing it, and deliverables come with documentation and knowledge transfer. The test is simple — if we stepped away, your own people could keep this running.
