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AI Development & Implementation

Practical AI that ships and scales

We build AI into real enterprise software using Microsoft's AI platform. From RAG-powered search and generative features to autonomous agents and multi-agent orchestrations. Not proofs of concept that gather dust, but production AI that delivers measurable business value.

AI capabilities across the spectrum

From generative AI features and intelligent automation to agentic AI and multi-agent systems. We build AI where it delivers proven value, connected to your data and integrated with your systems.

Generative AI Features

Document generation, content summarisation, intelligent chat interfaces, and natural language querying built on Azure OpenAI Service. We build generative AI that is grounded in your data, not generic model outputs.

Intelligent Automation

Workflow automation, decision support, and document processing powered by AI. Reduce manual effort, accelerate decision-making, and free your teams to focus on high-value work.

AI-Enhanced Applications

Recommendations, anomaly detection, predictive analytics, and smart search embedded into your existing or new applications. AI that improves the software your people already use.

Agentic AI and Multi-Agent Systems

Autonomous AI agents that reason, plan, and execute multi-step tasks across your systems. We build single agents for focused automation and multi-agent orchestrations where several specialised agents collaborate to solve complex workflows. Built on Semantic Kernel and Azure OpenAI with tool-calling, MCP integration, and human-in-the-loop controls.

Copilot Extensions

Microsoft 365 Copilot extensions and custom copilots built with Copilot Studio. We connect Copilot to your enterprise data and systems so it works with your context, not just general knowledge.

Built on the enterprise AI stack you can trust

As a Microsoft Solutions Partner, we build on the Azure AI platform. Your data stays in your tenancy, your compliance requirements are met, and you get access to the latest models as they ship.

Azure OpenAI Service

Enterprise-grade access to GPT models with full data sovereignty. Your data stays within your Azure tenancy and is never used to train public models.

Azure AI Search (RAG)

Retrieval-Augmented Generation connects your AI to internal knowledge bases, documents, and databases for accurate, context-aware responses.

Copilot Studio

Build custom copilots that integrate with Microsoft 365, Dynamics, and your own systems. Low-code orchestration with enterprise security built in.

Semantic Kernel

Microsoft's open-source SDK for building AI agents and multi-agent orchestrations in C#/.NET. We use it for tool-calling, planning, memory, and agent coordination across complex enterprise workflows.

Azure AI Document Intelligence

Extract structured data from documents, invoices, contracts, and forms. Automate data entry and document processing at scale.

Azure AI Foundry

The unified platform for building, evaluating, and deploying AI models. We use it for prompt engineering, model evaluation, and responsible AI testing.

Responsible AI for real organisations

Enterprise AI must be secure, cost-effective, and reliable. We address the concerns that matter most to CIOs, CTOs, and procurement teams.

Data privacy and residency

Azure OpenAI keeps your data within your chosen Azure region. No data leaves your tenancy, and nothing is used to train public models. For regulated industries, this is non-negotiable.

Responsible AI principles

We follow Microsoft's Responsible AI framework: fairness, reliability, privacy, inclusiveness, transparency, and accountability. Every AI feature includes evaluation criteria and human-in-the-loop controls where needed.

Cost management

AI workloads can become expensive without proper architecture. We design for cost efficiency: caching, token optimisation, tiered model selection, and monitoring dashboards that keep spending predictable.

Production-grade reliability

AI features must be as reliable as the rest of your application. We implement retry logic, fallback strategies, output validation, and comprehensive logging so AI failures are handled gracefully.

From assessment to production AI

We follow a structured approach that validates value quickly, then builds production-grade AI features in agile sprints.

1-2 weeks

AI Opportunity Assessment

We analyse your business workflows, data assets, and strategic goals using a task-based evaluation framework. You receive a prioritised roadmap of AI opportunities ranked by business impact, technical feasibility, and implementation effort.

2-4 weeks

Proof of Concept

We build a focused proof of concept that validates the approach with your real data. This gives you tangible evidence of value and surfaces any data quality or integration challenges before full delivery begins.

Build & Integration

Production AI development in agile sprints. We integrate AI capabilities into your existing or new applications, with security, compliance, and responsible AI controls embedded throughout. AI-augmented development tools accelerate our own delivery alongside the AI features we build for you.

Launch & Evolve

After deployment, we monitor performance, gather feedback, and iterate. Our model-agnostic architecture means you can adopt newer, better models as they emerge without rebuilding your solution. Managed support keeps your AI features running reliably.

Your extended AI team

Our permanent, UK-based team combines AI expertise with deep software engineering experience. Here's who will bring your AI vision to life.

A

AI Solutions Architect

Designs the AI architecture, selects models and retrieval strategies, and ensures the solution meets security and compliance requirements.

A

AI / ML Engineer

Implements the AI pipeline: data ingestion, embedding, prompt engineering, model integration, and response evaluation.

B

Backend Engineer

Builds the APIs, services, and integrations that connect AI capabilities to your existing applications and data sources.

D

Delivery Manager

Your main point of contact. Manages scope, timeline, and stakeholder communication to keep delivery on track.

B

Business Analyst

Translates business requirements into technical specifications, ensuring the AI solution solves the right problems.

Q

QA Analyst

Tests AI outputs for accuracy, reliability, and safety, including adversarial testing and evaluation against agreed criteria.

About Talk Think Do

Talk Think Do is a UK-based AI development company founded by Matt Hammond and headquartered in Bournemouth, Dorset. We are a Microsoft Solutions Partner with designations in Azure Infrastructure, Digital & App Innovation, and DevOps & GitHub.

We build AI into enterprise software using Azure OpenAI Service, Copilot Studio, Semantic Kernel, and Azure AI Search. Our clients include education publishers, government bodies, transport operators, and fitness brands who need AI that works in production, not just in demos.

AI is not just something we build for clients. It is embedded in how we work. Every engineer uses AI-augmented development tools daily, and our quarterly AI evaluation cycle keeps us at the leading edge of practical AI adoption.

AI-powered platform for charities and funders

AI platform uses Azure OpenAI to turn community activities into evidence-based impact assessments

Designed to streamline funding distribution, cut bureaucracy, and target resources at high-impact activities

Scalable architecture supports continuous evolution as AI capabilities advance

Frequently asked questions

How much does AI software development cost?

AI projects range from £30,000 for a focused proof of concept to £300,000+ for enterprise-wide AI integration. Costs depend on the complexity of the use case, data readiness, and integration requirements. We provide a detailed estimate after the AI Opportunity Assessment. See our pricing guide for more detail.

What is RAG and do I need it?

RAG (Retrieval-Augmented Generation) connects an AI model to your own data so it can give accurate, context-aware answers instead of relying on general knowledge. If your use case involves answering questions about internal documents, policies, or data, RAG is almost certainly the right approach. It is how we build most enterprise AI features.

Can AI be integrated into our existing software?

Yes. Most of our AI work involves adding intelligent features to existing applications, not building standalone AI products. We integrate via APIs, so AI capabilities can be added to web apps, mobile apps, internal tools, and enterprise systems without a full rebuild.

What's the difference between Copilot and custom AI?

Microsoft 365 Copilot provides AI assistance within Microsoft apps (Word, Teams, Outlook). Custom AI is purpose-built for your specific business processes and data. They complement each other: Copilot handles general productivity, while custom AI handles domain-specific tasks that generic tools cannot. We build both, and we extend Copilot with your data using Copilot Studio.

What is agentic AI and how is it different from a chatbot?

A chatbot responds to a single prompt and returns an answer. An AI agent can reason, plan, and execute multi-step tasks autonomously: calling APIs, querying databases, making decisions, and coordinating with other agents. Agentic AI is suited to workflows that involve multiple steps across multiple systems, such as processing a claim, onboarding a customer, or generating a report from several data sources. We build agents on Semantic Kernel with tool-calling and human-in-the-loop controls for enterprise safety.

Ready to put AI to work in your organisation?

Start with a free AI Opportunity Assessment. We identify where AI will deliver the greatest impact, and where it will not, so you invest in the right things.

Book a free consultation

or call 01202 375647