Adding AI without a rewrite
Most companies asking about AI already have a working product with real users. The requirement is not to rebuild it — it is to add capability without introducing latency, cost surprises or new failure modes into a system people depend on.
We integrate at the service boundary, so AI features fail independently of core functionality. If the model provider has an outage, your product keeps working and the AI feature degrades gracefully. That separation is a design decision that has to be made at the start.
- Integration at the service boundary, isolated from core paths
- Graceful degradation when providers are slow or unavailable
- Streaming responses so perceived latency stays low
- Token budgets, rate limits and abuse prevention from day one
- Provider abstraction so models can be swapped without a rewrite
The features that move product metrics
Summarisation of long content, semantic search that understands intent rather than keywords, drafting and content generation inside existing editors, document analysis and extraction, and classification or routing behind the scenes.
The common thread is that each removes a specific, measurable friction. We instrument the feature before launch so you can see whether it changed behaviour, rather than assuming it did.
- Summarisation and digest generation over long-form content
- Semantic and natural-language search across your data
- In-product drafting, rewriting and content generation
- Document parsing, extraction and structured output
- Background classification, tagging and routing
Controlling cost before it controls you
Token cost scales with usage, which means a successful feature can produce an unwelcome invoice. We design cost controls into the integration rather than reacting after the first surprising bill.
Model routing sends easy requests to cheaper models. Semantic caching prevents paying twice for near-identical queries. Prompt compression cuts context size without degrading output. On a recent engagement this combination reduced monthly spend by roughly 70% on identical traffic.
What you receive
- AI features integrated into your existing product
- Provider abstraction layer for model portability
- Cost controls: routing, caching and token budgets
- Monitoring for latency, error rate and spend
- Load-tested rollout plan with feature flags
Technologies we use
AI Integration Services — frequently asked questions
How much does AI integration cost?
A single well-defined AI feature added to an existing product typically runs $10K to $25K. Multi-feature programs range from $30K to $70K. Integration work is usually cheaper than greenfield AI development because the surrounding product, auth and data layer already exist.
Will adding AI slow our product down?
Not if it is architected correctly. AI calls run asynchronously off the critical path, responses stream so users see output immediately, and results are cached where appropriate. Users typically perceive AI features as faster than the underlying model latency suggests.
What happens if OpenAI or Anthropic goes down?
Your product keeps working. We build provider abstraction with fallback routing, so traffic can shift to an alternative model automatically, and AI features degrade gracefully rather than taking core functionality with them.
How do we keep token costs predictable?
Model routing, semantic caching, prompt compression and hard per-user token budgets, all instrumented on a spend dashboard with alerting. These techniques routinely cut costs by 50 to 70% versus a naive implementation sending every request to the largest model.
Can you work inside our existing codebase?
Yes. We work in your repository, follow your conventions, and submit pull requests your team reviews. Most integration engagements are collaborative rather than handover-based, precisely because your team knows the existing system best.
Talk through your ai integration services project
A free 30-minute call with an engineer — not a salesperson. You leave with a scope, a cost range and an honest view of the risks.
Book a free strategy call
