Product teams adding AI to a live product
Add useful AI capabilities to an application your customers already depend on without turning a promising demo into a fragile feature.
AI Integration Services
Add AI where your teamalready does the work
We integrate AI agents, copilots, and LLM capabilities into the software, data, and workflows your business already runs without asking your team to adopt another disconnected tool.
A focused 30-minute conversation about the systems, workflow, and outcome you want to improve.
Who this is for
For teams that need AI to work with the rest of the business
Operations teams working across too many systems
Connect the CRM, help desk, documents, databases, and internal tools that hold the context people need to do their work well.
Businesses modernizing without a full replacement
Introduce AI alongside established systems through deliberate APIs, permissions, and workflows instead of a risky rip-and-replace project.
Capabilities
AI that fits the systems you already run
The most useful AI is connected to the right context, tools, and controls, not isolated in a separate interface.
AI agents connected to business systems
Give an agent approved access to the tools and information it needs to prepare work, take bounded actions, and hand off exceptions.
Copilots inside the tools people use
Embed contextual assistance into a CRM, support workspace, internal portal, or SaaS product instead of sending users to another tab.
Knowledge and data connections
Connect governed documents, records, and approved data sources so AI responses have useful, current context and clear boundaries.
Document and intake workflows
Use AI to extract, classify, validate, and route information from forms, email, PDFs, and business documents into the right systems.
AI-assisted workflow steps
Combine deterministic rules with AI for classification, drafting, summarisation, and decision support where the workflow genuinely needs judgment.
Integration layers built to evolve
Create reliable connections, fallbacks, logging, and ownership boundaries so the system can adapt as models, tools, and business needs change.
What you get
A dependable integration, not just an API connection
A successful AI integration needs more than a model endpoint. We design the data path, system boundaries, user experience, controls, and operating details around the real workflow.
- Integration assessment, system map, and measurable success criteria
- Secure API, database, document, and event connections where appropriate
- AI interaction design, model routing, and structured-output patterns
- Access controls, approval flows, and clear action boundaries
- Testing for realistic scenarios, failures, and operational exceptions
- Monitoring, documentation, handover, and an improvement roadmap
Process and timeline
Start with a clear connection between systems and outcome
We make the integration path visible before building deeply into your environment, then ship in controlled working increments.
1. Map the systems and outcomeWeek 1
We identify the workflow, users, source systems, permissions, constraints, and business result that makes the integration worth building.
2. Design the integration pathWeek 1–2
We define the data and tool boundaries, integration approach, AI and rules-based steps, safeguards, and a realistic delivery scope.
3. Connect, build, and testWeeks 2–4
We build in working increments and test with realistic records, permissions, edge cases, and the people who will use the system.
4. Launch with visibilityWeeks 4–6+
We introduce the integration with observability, exception paths, ownership, and a plan for improving quality, cost, and adoption after launch.
Engagement model
Scope the connection before committing to the build
Integration scope depends on the systems involved, their APIs and permissions, the workflow's risk level, data sensitivity, and the reliability the team needs after launch.
Most engagements begin by assessing one valuable integration, defining the architecture and controls, then building a focused working system that can prove value.
What we will clarify on the first call
- The workflow and user experience you want to improve
- The systems, records, and access boundaries involved
- The decisions AI can support and the actions people should continue to approve
- The smallest integration that can show meaningful value
Integration and automation work together
Connect AI to the work, then automate the right next step
Integration gives AI the context and controlled access it needs. Automation turns a defined sequence of work into a dependable operating workflow. Some projects need one; many need both.
Explore AI automation servicesSecurity and data handling
Useful AI needs deliberate boundaries
We design integrations around the access, controls, and operational ownership appropriate to the workflow, not around a broad promise of unrestricted autonomy.
- Least-privilege access for every connected system and data source
- Explicit approval for sensitive, external, or irreversible actions
- Secure management of API keys, credentials, webhooks, and environments
- Role-aware data access and clear boundaries around model context
- Logs, alerts, and fallback behaviour for failures and exceptions
FAQ
Common questions about AI integration
What are AI integration services?
AI integration services connect AI capabilities, such as agents, copilots, retrieval, classification, or generation, to the applications, data, and workflows your team already uses. The goal is useful work inside the existing operating environment, not another disconnected AI tool.
Can you add AI without replacing our current software?
Usually, yes. We start with the systems that already hold the workflow and connect at the appropriate boundary through APIs, events, approved data access, or a purpose-built integration layer. If a system creates a real constraint, we make that clear rather than hiding it behind a brittle workaround.
What systems can you integrate with?
We assess the systems involved in your workflow, such as CRMs, support platforms, internal tools, databases, document stores, email, and APIs, then design the smallest, safest connection that can create value. The exact approach depends on the access and reliability each system supports.
How do you keep integrated AI safe and reliable?
We define what the AI can read, what it can do, and what must stay under human control. Depending on the workflow, that includes role-aware access, validation, structured outputs, approval gates, logging, fallbacks, evaluation cases, and alerts for exceptions.
How long does an AI integration take?
A focused integration can often move from assessment to a working system in a few weeks. The timeline depends on system access, data readiness, the number of connections, required approvals, and the level of production hardening the workflow needs.