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Microsoft Foundry, Copilot, Agentic AI

Exquitech designs, builds and governs enterprise AI on the Microsoft stack with the architecture, evaluation and controls that survive an audit.

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Overview

Most organizations have already proved that generative AI works in a demo. The hard part is what comes next: giving agents trustworthy access to enterprise data, making their answers explainable, keeping cost predictable, and governing a growing population of agents without losing control of identity, permissions and audit.

That is the work Exquitech does. We architect and deliver AI systems on Microsoft Foundry, Microsoft Fabric, Microsoft 365 Copilot and Copilot Studio grounded in a customer’s own data, semantics and security model. Our teams cover the full path: readiness and use-case selection, solution architecture, data and knowledge engineering, agent development, evaluation, secure deployment into an AI landing zone, and ongoing operation.

We are model-agnostic by design. The model is a component, not the strategy. What determines whether an AI system earns its place in production is the quality of the context it retrieves, the discipline of how it is evaluated, and the governance wrapped around it.

Background

Microsoft Foundry: the agent platform

  • Model choice, not model lock-in

    Foundry’s catalogue spans frontier and open-weight models; OpenAI, Anthropic, Mistral, Meta, xAI, Cohere and others behind one endpoint, one identity model and one Azure invoice, with usage counting toward existing Azure commitments. We benchmark candidate models per workload against the metrics that actually matter for that workload: reasoning depth, latency under real concurrency, multilingual quality, and cost per thousand documents or conversations. Frequently the right answer is a tiered design, a frontier model for the hard reasoning step and a fast, low-cost model for classification, extraction and routing.

  • Agents and orchestration

    We build single-purpose agents and multi-agent systems using Foundry Agent Service and the Microsoft Agent Framework, with the Model Context Protocol (MCP) for tool and data access and agent-to-agent (A2A) messaging for delegation between specialised agents. Typical designs pair a planner with domain agents, a retrieval layer, deterministic business logic where determinism is required, and explicit human-in-the-loop checkpoints.

  • AI Services for real-world inputs

    Enterprise processes rarely start with clean text. We combine Foundry models with Azure AI Services; Document Intelligence, Content Understanding, Vision, Speech and Language to handle scanned PDFs, photographs, forms, email threads and telephony audio, in Arabic and English.

  • Evaluation and observability as a deliverable

    Agents that cannot be measured cannot be trusted. Every engagement includes an evaluation harness: golden question sets for retrieval quality, groundedness and citation checks, offline metrics for predictive models, tracing across agent steps and tool calls, and regression gates before any prompt or model change reaches production. Continuous evaluation runs after go-live, not only before it.

  • Built for a real landing zone

    Production AI is an infrastructure problem as much as a model problem. We deploy into properly architected landing zones; private endpoints and network isolation, Entra ID and role-based access control, key and secret management, Purview-based data governance, cost guardrails, and regional data residency for customers with sovereignty requirements in the UAE, Saudi Arabia and the wider Gulf.

The Microsoft IQ layer: context is the differentiator

Two organizations can deploy the same model and get very different results. The difference is context. Microsoft’s intelligence layer gives agents governed, permission-aware access to what an organization knows, how it operates and what its data means. Exquitech designs and implements across all four layers.

  • Fabric IQ: what the business means

    The semantic layer over structured data in OneLake and Power BI. We model ontologies and semantic models so customer, order and approval threshold carry your own definitions, then expose Fabric data agents so people can query analytics in plain language.

  • Foundry IQ: what the organization knows

    The managed knowledge layer for unstructured content across Azure, SharePoint, OneLake and the web. One permission aware knowledge base replaces a pipeline per repository, with query planning, parallel search, permission checks and cited answers.

  • Work IQ: how work actually happens

    Signals from Microsoft 365: documents, meetings, chats and workflows. This lets an agent know who owns a process, what was decided last week and where a request sits, instead of treating every question as context free. Most AI projects miss it.

  • Web IQ: what is true right now

    Low latency ranked grounding in live external information, for market monitoring, competitor tracking, regulatory watch and supplier due diligence. It keeps answers current when the question depends on what is true outside your tenant today.

Microsoft 365 Copilot and Copilot Studio

Copilot has moved well beyond in app assistance. It is now an agent platform, and adoption programs that stop at license rollout leave most of the value untouched. Inside Copilot, reasoning agents change what the product is for, and Copilot Studio is now a distinct build platform, not a Copilot setting.

Reasoning agents inside Copilot

Microsoft 365 Copilot ships specialized reasoning agents that change what the product is for. We help organizations identify which roles and workflows these agents genuinely replace work in, and set the data boundaries they operate within.

  • Researcher

    Performs multi step research across email, meetings, files, chats, the web and connected line of business systems to produce sourced analysis.

  • Analyst

    Writes and runs Python against spreadsheets and datasets for forecasting, cohorting and visualization.

Copilot Studio: agents that act

Copilot Studio is now a distinct build platform, not a Copilot setting.

  • Computer using agents

    Operate desktop and web applications directly, filling forms, clicking through screens and completing UI tasks in legacy systems that expose no API, with managed credentials and model choice.

  • The redesigned workflows canvas

    Combines agent reasoning, API calls, approvals, business logic and UI actions on one surface, so deterministic steps stay deterministic.

  • Real time voice agents (generally available)

    Available through Dynamics 365 Contact Center, they handle inbound and outbound calls with caller identification, in conversation actions, and warm handoff to human agents with context preserved.

  • Model choice and interoperability

    A Copilot Studio agent can call Foundry models, MCP tools and Work IQ context, so low code and pro code agents share one architecture instead of two disconnected estates.

Agent governance: control before sprawl

The operational risk in 2026 is no longer building an agent. It is finding out, six months later, that your organization has dozens of them, built by different teams on different platforms, with no inventory and no audit trail.
Microsoft Agent 365, generally available since May 2026, is the control plane for that problem. Our position is simple: governance is designed in at the first agent, not added at the fiftieth.

Background

Our agent governance work covers

Six areas we set up before your agent estate grows.

  • Agent inventory and ownership

    A known list of every agent in your estate, with a named owner and a named sponsor for each one.

  • Identity and least privilege

    Managed identities, least privilege access and safe credential handling for autonomous agents.

  • Data boundaries

    Sensitivity labeling and permission aware retrieval, so agents only reach the data they should.

  • Monitoring and cost

    Monitoring, cost attribution and behavioral risk signals for every agent you run in production.

  • Audit and evidence

    Audit trails, retention and evidence that is ready for regulators and for internal audit teams.

  • Shadow AI discovery

    Finds unmanaged agents on devices and third-party platforms and brings them into one inventory.

Background

Our Intellectual Property

Insights HQ: decision intelligence

A complete analytics and decision support platform built on Microsoft Fabric, Azure Machine Learning and Power BI, with a conversational assistant for plain language questions.

Explore Insights HQ

ExqGUARD: the semantic layer for agents

An MCP based API that gives agents governed enterprise data and business actions through a permission aware interface, so they can run in regulated environments.

Explore ExqGUARD

Packaged Solutions

Ready to deploy accelerators with published architectures and predictable running costs: recruitment automation, contract and NDA review, investigation reports, a SharePoint knowledge assistant, campaign intelligence and internal audit automation.

Explore our Packaged Solutions

What we build

A selection of the solution patterns our AI team delivers for enterprise and government customers across energy, aviation, hospitality, real estate, retail and ecommerce, insurance, legal, construction, and the public and nonprofit sectors.

  • Multi Agent Process Automation

    Agent systems that run a full business process from start to finish. For example, an internal audit lifecycle covering scoping, planning, fieldwork and report writing, with specialized agents per stage, shared working memory and human sign off gates.

  • Governed Policy and Decision Assistants

    Assistants over policy, regulatory and authority documents that answer with the exact governing clause cited. Examples include accounting policy interpretation, delegation of authority checks and committee advisory assistants, all captured for audit.

  • Intelligent Document Processing at Scale

    High volume document validation combining Document Intelligence with reasoning models: completeness and signature checks, field extraction, cross document validation, discrepancy detection, exception queues and human review in your own tools.

  • Customer facing Chat and Voice Agents

    First line customer service across WhatsApp, web chat, email and voice, with retrieval over product and order data, live quality monitoring, correction workflows and guaranteed escalation to human agents. Conversational commerce can follow later.

  • Industrial and IoT Intelligence

    Time series platforms over sensor and operational data: production forecasting, anomaly detection and event correlation across assets, plus plain language questions grounded in an ontology of your physical estate, so managers stop opening reports.

  • Customer and Product Machine Learning

    Behavioral and value based segmentation, churn prediction and survival analysis, customer lifetime value, propensity and next best action models, recommender systems, and customer entity resolution and deduplication across fragmented source systems.

  • Vision and Safety

    Visual search over large image catalogs, image classification, and automatic classification of health, safety and environment observations by category and severity, with retraining pipelines and model comparison built into daily operations, not a one off.

  • Arabic and English Language Solutions

    Bilingual agents for document generation, rewriting and style compliance against your own language standards, integrated with SharePoint and Microsoft 365. It is a capability that consistently sets regional delivery apart from English first implementations.

  • AI Readiness, Architecture and Enablement

    Use case discovery and prioritization, feasibility assessment, solution architecture and cost modeling, AI landing zone design, data and AI governance, access and RBAC remediation, and enablement for the teams who will run the platform after handover.

What customers get

What you gain when your AI runs on a governed Microsoft foundation.

  • Enhanced productivity

    Validation, review and reporting work measured in days moves to minutes, and your people move to exceptions and judgment.

  • Answers Grounded in your own Semantics

    Agents use your definitions of customer, revenue, entity and approval, not a generic interpretation.

  • Auditability by Design

    Cited sources, captured decisions, traced agent steps and evidence that satisfies internal audit and regulators.

  • Predictable Cost

    Right sized model tiers, measured token use and infrastructure sized to the real workload, not the worst case.

  • Sovereignty and Compliance

    Regional data residency, private networking and least privilege access as defaults.

  • Scale without Sprawl

    A governed agent estate with a known inventory, clear ownership and enforced boundaries.

Background

Ready to move your AI into production?

Tell us the process you want to change. We will map the path from readiness to governed agents.

Related Capabilities

OpenAI & Copilot Adoption

Roll out Copilot and AI tools with a proven plan, champions, training and adoption tracking.

Explore OpenAI and Copilot Adoption

Secure AI Adoption

Scale AI safely with governance, data protection, model security and MLOps built in from day one.

Explore Secure AI Journey

Modern Workplace

Get the most from Microsoft 365, the home of Copilot, with better collaboration, stronger security and higher productivity.

Explore Modern Workplace

Speak to an expert about your AI roadmap

Our AI team is ready to help you move from AI experiments to governed agents in production. Let’s chat.

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