Useful automation
Focus AI on repeatable work where speed, consistency and human oversight can be measured.
Generative AI
AI Integration
Add a working AI feature to the product you already have—copilot, search, or automation—grounded in your data, resilient to provider outages, and shipped without a rebuild.
Practical AI capabilities inside existing software.
- Human-in-the-loop
- controls
- Evidence-led
- evaluation
- Secure
- integration
01
Quick answer
What is AI Integration?
AI integration adds a focused model capability to an existing product or workflow. It can include model and provider selection, retrieval, tool connections, prompt and policy design, evaluation, security controls, observability, and user-experience changes.
- AI features added without a platform rebuild
- Grounded retrieval, guardrails & citation support
- Resilient to provider outages: retries, fallbacks & queues
- Multi-provider architecture kept replaceable
Business outcomes
Built to add one working AI feature, not a rebuild.
Integr8e approaches ai integration as a business capability—not an isolated technical task. Priorities stay connected to the users, operations and results behind the work.
Grounded answers
Connect models to approved business knowledge so responses are relevant and traceable.
Responsible adoption
Design permissions, evaluation and review controls around real operational risk.
Production readiness
Build for observability, cost control, model change and dependable integration.
What we deliver
AI integration services from architecture assessment to live feature.
One connected team covers the decisions and delivery work needed to move from uncertainty to a dependable outcome.
Use-case strategy
Prioritise opportunities by business value, data readiness, risk and implementation effort.
Retrieval & knowledge
Prepare governed information and retrieval flows that keep answers grounded.
Agentic workflows
Connect models, tools and approval steps to complete useful multi-stage work.
AI product engineering
Create intuitive interfaces and robust services around model capabilities.
Evaluation & guardrails
Measure answer quality, safety, latency and cost before and after release.
AI operations
Monitor usage, failures, model behaviour and emerging opportunities over time.
Is this the right fit?
Best suited for product and engineering leaders adding one focused AI capability to an existing application instead of building a new product around it.
Not every challenge needs the same team or solution. We start by testing the business case, current constraints and fastest credible route to value—then recommend a scope that fits the evidence.
Delivery architecture
A controlled path from architecture assessment to a shipped AI feature.
The plan adapts to your context, while short feedback loops and visible milestones keep the engagement controlled.
- 01
Frame
Define the task, user, acceptable output, risk boundaries and measurable value.
- 02
Prepare
Assess data, permissions, integrations and the evaluation set required for confidence.
- 03
Prototype
Test the workflow quickly with representative inputs and real user feedback.
- 04
Productionise
Add guardrails, observability, security and reliable system integrations.
- 05
Evaluate & evolve
Track quality and cost continuously as models, data and needs change.
Technology context
Tools chosen around the problem—not the trend.
We select platforms against security, scale, team fit, integration needs and the full cost of ownership.
- OpenAI
- Python
- AI Agents
- Node.js
- PostgreSQL
- MongoDB
- Redis
- Docker
- AWS
- Azure
Frequently asked questions
Answers about ai integration.
Need an answer specific to your environment? Share the context and our team will help you identify a practical next step.
What does AI integration involve?
AI integration adds a focused model capability to an existing product or workflow. It can include model and provider selection, retrieval, tool connections, prompt and policy design, evaluation, security controls, observability, and user-experience changes.
Can AI be added without rebuilding our existing application?
Often, yes. A separated AI service or integration layer can connect to the existing application through APIs or events. We first assess the current architecture, data access, latency needs, and failure modes to determine the smallest safe change.
How do you protect sensitive data sent to an AI model?
Controls can include data minimisation, redaction, tenant isolation, encryption, scoped credentials, retention settings, regional processing choices, and access logging. Provider and deployment decisions follow the organisation's legal, security, and contractual requirements.
How do you reduce inaccurate or ungrounded AI responses?
We constrain the task, supply approved context, require citations where useful, validate outputs, create evaluation cases, and provide fallback or human-review paths. No model is treated as perfectly accurate, so the product is designed around known uncertainty.
Can an AI integration support more than one model provider?
Yes. Where continuity or model flexibility matters, we can isolate provider-specific code behind a shared interface. Actual portability depends on differences in model behaviour, tools, context limits, safety controls, and hosting requirements.
What happens when the AI provider is unavailable?
The integration can use timeouts, retries, circuit breakers, queues, fallbacks, and clear user messaging according to the workflow's importance. Critical business actions should fail safely instead of proceeding from an incomplete model response.
How much does AI integration cost?
Cost depends on the complexity of the AI feature, the number of systems it touches, and the evaluation and guardrail work required. A single well-scoped feature added to an existing application costs less than a multi-provider integration with several downstream systems. We scope the use case first and provide a fixed estimate rather than a generic day rate.
How long does it take to integrate AI into an existing product?
A focused, single-feature integration typically reaches a production-ready release in six to ten weeks, including evaluation. Integrations touching several systems, requiring multi-provider support, or needing extensive guardrail work take longer. Timing is confirmed after assessing the existing architecture.
Start with the outcome
Let's make your ai integration initiative concrete.
Tell us what needs to change, who it affects and where the current approach falls short. We'll help shape a sensible next step.
hello@integr8e.com