Service AI Engineering
We integrate AI into your existing app with prompt caching, structured output, cost dashboards, and fallback wiring. Your team gets a feature that ships, not a science project.
Why this matters
The first feature ships in a sprint. By the third, your codebase has three different retry patterns, two cost dashboards that disagree, and no one knows which prompts are cached. We build an AI layer that scales past the first feature, with the observability and cost controls a real production system needs.
An integration, end to end
One call from your existing app, Claude does the work, response is typed and validated before it touches your code. Below is the shape we drop in. Scroll up and back down to replay.
Summarize this support ticket thread and extract the top-3 intent labels.
What we build
Prompt caching, structured output, observability, cost guards, fallback wiring. Every integration ships with the operational pieces a real production system needs.
01
System prompts and long context cached on every call. Cache hit rate measured on your real traffic rather than assumed. Repeat context is not paid for twice, which is where most of an unoptimised token bill goes.
Cost per AI call drops from cents to fractions of a cent.
02
AI calls return typed data, not strings to parse. JSON schema enforcement at the model layer. Your downstream code never crashes on a hallucinated key.
Output validation errors fall to near zero.
03
Per-feature, per-user, per-tenant token spend tracked in your existing observability stack. Alerts fire before bills surprise you, not after.
No more surprise bills at end of month.
04
Server-sent events, partial JSON parsing, optimistic UI. The AI feature feels like part of your app, not a third-party iframe with a spinner.
Perceived latency drops from seconds to milliseconds.
05
Primary model down? We fall back to a secondary model or cached response automatically. SLA holds even when Anthropic, OpenAI, or your inference provider has an outage.
AI features stay up during model provider outages.
06
OpenTelemetry traces on every model call. Per-prompt latency, cost, cache utilization, and quality metrics in your existing dashboards. Debug is grep, not vibes.
Mean time to debug a bad output under 10 minutes.
Cached
from the first integration, not bolted on after the first bill arrives
Prompt caching, batching where latency allows, and per-call cost logging so you can see what each feature costs to run.
The observability layer
OpenTelemetry traces with cost, latency, cache utilization, and token counts. Per-feature dashboards. Alerts before bills surprise you. Debug is grep, not vibes.
// observability/ai-trace.ts
import { trace } from '@opentelemetry/api';
export async function tracedCompletion(input: PromptInput) {
return trace.getTracer('ai').startActiveSpan('completion', async (span) => {
const start = performance.now();
try {
const response = await client.messages.create(input);
span.setAttributes({
'ai.model': input.model,
'ai.input_tokens': response.usage.input_tokens,
'ai.cache_read_tokens': response.usage.cache_read_input_tokens ?? 0,
'ai.output_tokens': response.usage.output_tokens,
'ai.cost_usd': calculateCost(response.usage, input.model),
'ai.latency_ms': performance.now() - start,
});
return response;
} finally {
span.end();
}
});
}
Process
01
One to two weeks. We audit your existing app, identify the workflows where AI fits, design the prompt and output contract, and lock the cost ceiling. You approve the spec before any code.
Fixed scope, fixed price.
02
Three to six weeks. The AI layer ships behind a feature flag. Cache, observability, structured output, and cost guards in from commit one. Staging available within seven days.
You can use the feature in week two.
03
One to two weeks. Canary rollout, cost and quality dashboards live, on-call coverage during the first 30 days. Handoff docs and team training before we step back.
Your team owns the AI layer at the end.
What it costs
Adding a model to one screen and wiring it through an application with your data behind it are the same sentence and very different projects. Here is what moves the number.
What moves the number
Small, well-defined work is usually better handled as an hourly block than scoped as a project, and we will say so rather than inflate it into one.
Rough idea to delivery
Quick enquiry
Tell us what the app does and what you want the model to do inside it. If you are worried about cost or accuracy, say which, because they pull in different directions.
Prefer the full form? Start a project
Common questions
Depends on the workload. Claude Sonnet 4.5 for reasoning, code, and agentic workflows. Haiku for high-volume classification or extraction. OpenAI for some tool-calling patterns. Open-weight (Llama, Qwen) for data residency or cost ceilings. We pick based on the actual job, not the brand.
Prompt caching, model routing, per-tenant rate limits, hard cost caps per user, and a real cost dashboard before launch. Most of our integrations cost under one cent per user request in production.
Structured output with schema validation handles most of it. Retrieval grounding for factual responses. A separate validator pass for high-stakes outputs. We agree on a quality bar with you and write the evals to enforce it.
Yes to all three. We have shipped AI features into Laravel apps, WordPress plugins, Astro frontends, and bare Node services. The AI layer is wire-protocol agnostic.
PII filtering before the model call, configurable data residency (Anthropic regions, OpenAI EU, on-prem inference), no training opt-in by default. We document the data flow and review it with your legal team before launch.
Integration work is estimated against scope rather than sold at a list price. Adding a model to a single screen and wiring one through an existing product with your own data behind it are very different projects. The section above sets out what moves the number.
Where to go next
Ready to add AI without the chaos?
Discovery call is free. Fixed-price quote within 48 hours. NDA on request.
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