AI Integration for ExistingSoftware
Add AI to the product or internal system you already run, through clean service boundaries and a gradual rollout, with no rewrite and no big-bang release.
AI Integration for Existing Software: what the work involves
Most companies do not need a new platform; they need their existing application to do something smarter. The system is years old, the database has quirks, authentication is custom, and the team that built it has moved on. Bolting a model onto it risks slow pages, leaked data and a feature that breaks whenever the vendor changes an endpoint. Engineers hesitate, and the AI idea sits in the backlog.
DevKey treats integration as software architecture. We study your codebase, data model and deployment, then place the AI capability behind a small service with a stable contract, so the main application calls it like any other dependency. Permissions are enforced with your own user model, prompts and model versions live in configuration, and every call has a timeout and a non-AI fallback. Features ship behind flags to a small group first, with logging and cost tracking, and we leave your team with documentation they can maintain.
Core features
Codebase and data review
We examine architecture, data access and constraints to find the safest integration points before any design is proposed.
AI service with a stable contract
A separate service exposes well-defined endpoints for tasks such as summarising or classifying, isolating model details from your core code.
Permission-aware data access
Retrieval and tool calls respect the logged-in user's rights through your existing authorisation, so the AI cannot reveal what the user could not already open.
Fallbacks and timeouts
If the model is slow, down or uncertain, the application falls back to the previous behaviour instead of showing an error.
Feature flags and staged rollout
New capabilities are enabled for a pilot group, measured, and expanded as confidence grows, with an immediate off-switch.
Usage, quality and cost telemetry
Dashboards show calls, latency, failures, feedback and spending per feature, so owners can decide where to invest.
What we get right before launch
Legacy constraints
Old frameworks, tight coupling and unclear data semantics slow integration. We prefer thin adapters over invasive refactors, and we are upfront when some preparatory cleanup is unavoidable.
Vendor dependence
Hard-coding one provider's API throughout the application creates lock-in. A small abstraction layer and stored prompts let you change models or providers with limited rework.
Shared responsibility for failures
When AI-driven features misfire inside your product, your users blame you, not the model vendor. We define review, error handling and support responsibilities as part of delivery, and test failure paths, not just the happy case.
Tools and technology
- Python
- FastAPI
- Node.js
- OpenAI GPT
- Anthropic Claude
- LangChain
- PostgreSQL
- pgvector
- Docker
Common questions, answered
Do we have to rewrite our application?
Almost never. We add an AI service beside it and connect through APIs, queues or database views. Your core application changes only where it calls the new capability or displays its results.
Can you work with our stack, such as Laravel or .NET?
Yes. The AI service is separate and language-independent, and the integration side is plain HTTP or messaging. We pair with your developers and respect your conventions and deployment process.
How do you protect our users' data?
We send only the minimum necessary to the model, mask identifiers where possible, choose providers without training on inputs, and apply your permissions to every retrieval. Data flows are documented for your security review.
What if the AI feature does not work well?
Staged rollout and flags limit the damage, and fallbacks keep the old behaviour. Telemetry and feedback show what is failing, and we iterate or switch the feature off without disturbing the rest of the system.
More AI Agents & Automation services
All AI Agents & Automation servicesMLOps and AI Deployment
Turn a promising notebook or demo into a service that deploys repeatably, is monitored for quality and cost, and can be rolled back when it misbehaves.
LLM Integration Services
Add summarising, extraction, drafting or classification to the software you already run, built as an engineered feature with tests, limits and logs.
AI Proof of Concept Development
Find out quickly and cheaply whether an AI idea works on your own data, with measurable criteria agreed before a line of code is written.
Ready to start your AI Integration for Existing Software project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.