AI integration that adds intelligence to the product you already run.
You don't need to rebuild to benefit from AI. We integrate AI features into your existing product and stack, behind a model-agnostic layer with the cost, reliability, and security controls a live product needs, so you get the value without the risk of a rewrite.
AI, without starting over
Add AI to what works, don't replace it
You already have a product and a stack your team knows. Adding AI shouldn't mean throwing that away. Most AI value comes from a few well-placed features inside the software you already run, not from a rebuild that puts everything at risk.
We integrate AI into your existing product cleanly: providers behind an abstraction you control, your data connected for grounding, and guardrails for cost and reliability. It fits your codebase rather than fighting it, and it's built so a fast-moving model landscape never forces a rewrite.
What you get
AI woven into your stack, not bolted on
Provider integration, your data connected, and the controls that keep a live feature safe.
AI features in your existing product
AI added to the app you already run, search, generation, or classification, through typed integration, without a rebuild.
Model provider integration
Providers like Anthropic Claude and OpenAI wired in behind a clean abstraction, so the rest of your codebase stays simple.
Your data connected to AI
Embeddings and retrieval over your own content, so AI features answer from your data instead of generic model knowledge.
A provider-agnostic layer
One internal interface over the models, so you can switch providers or route by task without rewriting the app around them.
Fallbacks and reliability
Retries, timeouts, and graceful degradation, so a provider hiccup slows a feature instead of taking your product down with it.
Cost and rate-limit management
Caching, quotas, and budget controls, so AI usage stays predictable and a spike in traffic doesn't become a spike in your bill.
Security and privacy controls
Encrypted key management, careful data handling, and in-region or private endpoints where compliance requires them.
Observability
Tracing for requests plus cost, usage, and quality monitoring, so you can see how the AI behaves and catch regressions early.
How we build it
From audit to AI running in your product
A clear path from reviewing your stack to a hardened, monitored integration, reviewed at every stage.
- 01
Discovery & audit
We review your stack and the feature you want to add, then choose an integration approach that fits how your product is built.
- 02
Design
We decide providers, the abstraction layer, data flow, and guardrails up front, so the integration is deliberate, not ad hoc.
- 03
Build
We integrate behind typed, tested interfaces, shipped in reviewable iterations you can try in your product as they land.
- 04
Harden
We add fallbacks, rate limits, security, and cost controls, and run evals so the feature is reliable, not just working once.
- 05
Launch & support
We deploy, monitor cost and quality, and adapt as providers and models change, so the integration keeps working over time.
- TypeScript
- Python
- Anthropic Claude
- OpenAI
- pgvector
- REST APIs
Why it matters
The risk isn't adding AI, it's adding it badly
Bolting a model call straight into your app is quick, and it's how teams end up locked to one provider, blindsided by costs, and taken offline when that provider has a bad day. The shortcut that ships in an afternoon becomes the liability you manage for years.
A clean integration avoids all of that. A provider-agnostic layer keeps you free to switch models as the field moves, cost controls keep spend predictable, and fallbacks keep your product up. You get AI where it helps, on foundations that stay yours to change.
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FAQ
AI integration, answered
Want AI in your current product? Book a call and we'll scope the integration with you.
What does AI integration involve?
Adding AI capabilities to software you already have: connecting a model provider, wiring AI features into your app through typed interfaces, connecting your data for retrieval, and putting the reliability, cost, and security controls around it that a production feature needs. The point is to get AI value from your current product without rebuilding it.
How is this different from AI application development?
AI application development builds a new AI-powered application from the ground up. AI integration adds AI into a product that already exists. If you have a working app and want to give it AI features, that's integration; if you're building something new with AI at its core, that's application development.
Can we switch AI providers later, or use more than one?
Yes, and we design for it. We put a provider-agnostic layer between your product and the models, so you can switch providers, add a second for redundancy, or route different tasks to different models, without rewriting the features that depend on them.
How do you keep AI costs and reliability under control?
We cache repeated work, set quotas and budget alerts, and route simpler tasks to cheaper models, so cost stays predictable. For reliability we add retries, timeouts, and fallbacks, so a provider outage degrades a feature gracefully rather than breaking your product.
Is our data safe when we send it to an AI provider?
We treat it as sensitive by default: encrypted key handling, least-privilege access, and provider configurations that keep your data from being used for training without your explicit agreement. Where compliance requires it, we use in-region or private endpoints.
Can you integrate AI into any tech stack?
In most cases, yes. We work across common web and backend stacks and integrate through APIs, so the model providers sit behind a clean interface regardless of what your product is built in. We confirm the specifics during the initial audit.
Let's build something worth shipping
Tell us what you're working on. Book a call to talk through it, or send a few details and we'll get back to you with next steps.