Sense AI · Enterprise AI engineering

Build AI around a real workflow, then prove it deserves to operate.

Sense AI turns knowledge, documents, and multi-step work into governed services. We define the operating outcome, engineer the evidence and action boundaries, evaluate representative cases, and establish ownership before production release.

Frame an AI workflow See service families

Starting point: A workflow, decision owner, source boundary, and measurable outcome
Delivery: Readiness sprint, bounded pilot, production build, or managed improvement
Release: Representative evaluation, tested controls, operator readiness, and accountable approval

01 · Service families

Start with the constraint that prevents a workflow from being reliable today.

The families can be purchased independently or sequenced. Assurance and operations are embedded in every production build, and can also repair an existing pilot.

AI portfolio and readiness

Prioritise workflows, test feasibility and consequence, establish platform and data prerequisites, and define evidence-gated investment decisions.

Knowledge systems and copilots

Build permission-aware retrieval, citation, correction, and corpus operations for service, policy, engineering, or case knowledge.

Intelligent document processing

Convert document populations into validated records with field-level evidence, deterministic checks, and exception review.

Agentic process automation

Engineer bounded workflows with scoped tools, explicit state, approval, compensation, and operator intervention.

AI assurance and evaluation

Create representative cases, quality and control thresholds, release dossiers, exception decisions, and change-triggered review.

AI operations and improvement

Instrument workflow outcomes, detect dependency and quality failures, rehearse response, and improve from observed cases.

02 · Buyer journeys

A useful first engagement answers the next investment or operating decision.

We shape the first phase around the decision you need to make, while exposing dependencies that belong to Cloud foundation, security, data ownership, or process design.

Executive portfolio

Compare candidate workflows through value, feasibility, consequence, foundation readiness, and learning value.

Prioritised portfolio and investment gates

Process owner

Map one costly or unreliable workflow, its evidence, exceptions, approvals, and target service measure.

Workflow contract and bounded pilot brief

Technology and security

Assess identity, data, integration, model, evaluation, observability, and risk-control readiness.

Foundation gaps and assurance plan

Existing pilot

Reproduce failures, measure representative cases, close authority gaps, and define a production release decision.

Repair backlog and release dossier

03 · Responsibility model

Production readiness fails when platform, workload, and business authority are treated as one vague AI problem.

Sense Cloud can establish the Azure foundation. Sense AI engineers the workload and proof system. The customer remains the authority for sources, policy, decisions, and accepted residual risk.

Sense Cloud

Subscriptions, network and private access, Entra ID and RBAC, Key Vault, Foundry and Search foundation, Monitor, quota, policy, resilience, and platform operations.

Secure, observable Azure foundation

Sense AI

Workflow architecture, retrieval and orchestration, prompts and tools, approval experience, application integration, domain evaluation, workflow telemetry, and AI runbooks.

Working service plus release and operating evidence

Customer

Source authority, access policy, domain reviewers, action authority, funding, legal and risk decisions, acceptance thresholds, and accountable service ownership.

Named owners and recorded decisions

04 · Technical view

The model is one component inside an operating system of evidence and control.

The visual makes the causal path explicit. If identity, evidence, policy, or approval cannot be established, the workflow stops or returns control instead of improvising authority.

Illustrative reference patternBounded workflow anatomy

Identity + intentUser, role, permitted purpose

Grounded contextAuthoritative, current, allowed

Decision boundaryPolicy, confidence, consequence

AssistEvidence attached

ApproveNamed decision owner

StopRefuse or escalate

Illustrative pattern. A production workflow connects identity, authoritative context, policy, and human authority before an outcome is accepted.

Context is governed

Retrieval respects identity, source authority, freshness, and conflict policy.

Actions are separated

Planning does not grant execution authority; approvals and deterministic policy remain outside generated text.

Outcomes are inspectable

Evidence, decisions, receipts, exceptions, and interventions remain available to reviewers and operators.

05 · Engagement contract

Begin small enough to learn, but complete enough to test the production question.

A readiness sprint can lead to a bounded pilot, a production build, or a stop decision. Every phase has explicit inputs, outputs, exclusions, and an accountable decision at its end.

Readiness sprint

One to three workflows, foundation and source review, risk framing, feasibility probes, and a decision-ready roadmap.

Bounded pilot

Representative cases, constrained integrations, measurable workflow outcomes, and explicit no-production assumptions.

Production service

Enterprise identity and data controls, release evaluation, observability, runbooks, support ownership, rollback, and change governance.

Engagement contract

What must be true, who owns what, and what leaves the engagement.

A good fit when

Teams choosing where AI investment should begin
A process owner with one expensive or unreliable workflow
An existing pilot that cannot yet pass production review

Required before delivery

A named business and technical owner
Access to representative workflow cases and source owners
Agreement on the decision this phase must enable

Customer owns

Authorise access to people, systems, and representative data
Name source, policy, risk, and action authorities
Approve acceptance thresholds and residual risk

Delivery outputs

Workflow and responsibility contract
Foundation, data, integration, and assurance plan
Evidence-gated roadmap with a proceed, repair, or stop decision

Not included by default

Azure tenant, network, identity, Foundry, Search, monitoring, or platform foundation unless Sense Cloud is included in scope
A production guarantee based on a prototype or vendor benchmark
Unrestricted autonomous action or implicit approval authority
Customer policy, source ownership, or risk acceptance decisions

Questions to answer first

Which decision or task should become materially better?
Who can approve the sources and any resulting action?
What failure would make this workflow unacceptable?

The next step is a bounded working session: one workflow, its evidence sources, the decision owner, and the conditions under which the system must stop or hand over.

Frame an AI workflow