Wire an LLM into your app. One week.
OpenAI, Claude, or Gemini — integrated cleanly into your existing stack. Prompt caching, rate-limit handling, streaming where it helps. $1,999. Ships in seven days.
API Integration Sprint — a 7-day engagement that wires one LLM feature (chat, RAG answerer, summarization, classification, extraction, embeddings) into your existing stack (Rails / Node / Python / Django / WordPress / .NET / Go / Ruby), ships with production-grade features on from day one (prompt caching for 90% cost reduction on cached prefixes, rate-limit handling with exponential backoff, streaming where the UX benefits, retries for transient failures, timeouts so a hung provider call cannot starve your pool, dead-letter queue for hard failures, token + cost observability wired to your logging), and closes with a readable README your team can extend. $1,999 fixed, one week, one integration, handoff included.
Five situations the sprint handles.
If you're in one of these five shapes, the sprint is likely the right fit. If you're in more than one, stack sprints or book an AI Agent MVP (multi-feature scope).
| Situation | What's missing today | What the sprint ships |
|---|---|---|
| You have a product and want to add one LLM-powered feature | No in-house LLM experience; unclear which model, which SDK, which caching strategy | One-week sprint: scoping, integration, caching, streaming, observability, handoff |
| Team tried a `fetch()` wrapper and it melted under real traffic | No rate-limit handling, no retry, no streaming, no caching — brittle demo-quality code | Production-grade rewrite with exponential backoff, cache_control / prefix caching, and streaming |
| You need to pick between OpenAI, Anthropic, or Gemini and don't know the tradeoffs | Provider choice is a strategic decision; costs, capabilities, and migration paths differ | Model recommendation in Day-1 scoping call with cost/latency/quality numbers for your workload |
| Existing wrapper works in dev; in prod it's slow, expensive, or drops requests | No token budget alerts, no timeouts, no dead-letter path for failed generations | Observability wired to your logging stack + cost telemetry per endpoint |
| You need to ship an LLM feature before a specific launch / demo / raise | Hard deadline; in-house hire is 90+ days away; agency quotes are 6 weeks | Fixed-price, fixed-scope, 7-day delivery with a usable handoff README |
The 7-day integration-sprint schedule.
Four phases fitting into one week: scoping + access, core integration + caching, streaming + error handling, observability + handoff. Every phase ends with a demo-able artifact you can review.
- D1day 1
Scoping call + access
30-minute scoping call on day 1. We confirm which LLM feature (chat, summarization, extraction, classification, embedding search), which provider fits your use case, and which stack we'll wire into. We get read-only repo access, provider API keys in a secure vault, and confirm success criteria. By end of day 1 you have a signed-off spec.
- D2day 2–3
Core integration + caching
Day 2–3 is the core integration — SDK wired in, prompt templates structured, cache_control (Anthropic) or prefix caching (OpenAI) applied to the repeated segments, rate-limit handling via exponential backoff. First end-to-end round-trip works from your app's UI.
- D4day 4–5
Streaming + error handling
Day 4–5 we add streaming where the UX benefits (token-by-token chat, progressive long-form, live summaries), wire retries for transient failures, build a dead-letter path for hard failures, and add timeouts so a hung provider call cannot starve your request pool.
- D6day 6–7
Observability + handoff
Day 6–7 we hook token usage and cost telemetry to your logging stack (Datadog, CloudWatch, Sentry, custom), write the README your team will actually read, and do a 45-minute handoff call. You get the PR, the README, the observability dashboard link, and a usable integration you can extend.
- D1day 1
Scoping call + access
30-minute scoping call on day 1. We confirm which LLM feature (chat, summarization, extraction, classification, embedding search), which provider fits your use case, and which stack we'll wire into. We get read-only repo access, provider API keys in a secure vault, and confirm success criteria. By end of day 1 you have a signed-off spec.
- D2day 2–3
Core integration + caching
Day 2–3 is the core integration — SDK wired in, prompt templates structured, cache_control (Anthropic) or prefix caching (OpenAI) applied to the repeated segments, rate-limit handling via exponential backoff. First end-to-end round-trip works from your app's UI.
- D4day 4–5
Streaming + error handling
Day 4–5 we add streaming where the UX benefits (token-by-token chat, progressive long-form, live summaries), wire retries for transient failures, build a dead-letter path for hard failures, and add timeouts so a hung provider call cannot starve your request pool.
- D6day 6–7
Observability + handoff
Day 6–7 we hook token usage and cost telemetry to your logging stack (Datadog, CloudWatch, Sentry, custom), write the README your team will actually read, and do a 45-minute handoff call. You get the PR, the README, the observability dashboard link, and a usable integration you can extend.
A sample excerpt from a shipped integration.
This is the shape of code we ship on the Anthropic track: prompt caching via cache_control, streaming token-by-token, token-usage telemetry hooked to your logging. The OpenAI track looks similar with prefix caching; the Gemini track uses context caching.
01// src/lib/llm/claude.ts02// One-file integration: prompt caching, retries, streaming, observability.03import Anthropic from "@anthropic-ai/sdk";04import { logUsage } from "@/lib/observability";05 06const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });07 08const SYSTEM_PROMPT = [09 {10 type: "text",11 text: loadSystemPrompt(), // ~12KB repeated on every call12 cache_control: { type: "ephemeral" }, // 90% cheaper on cache hits13 },14];15 16export async function* answerQuery(userId: string, query: string) {17 const stream = await client.messages.stream({18 model: "claude-sonnet-4-6",19 max_tokens: 1024,20 system: SYSTEM_PROMPT,21 messages: [{ role: "user", content: query }],22 });23 24 for await (const chunk of stream) {25 if (chunk.type === "content_block_delta" && chunk.delta.type === "text_delta") {26 yield chunk.delta.text;27 }28 }29 30 // Cost + cache-hit telemetry — wired to your logging stack31 const final = await stream.finalMessage();32 logUsage({33 userId,34 route: "/api/answer",35 model: final.model,36 inputTokens: final.usage.input_tokens,37 outputTokens: final.usage.output_tokens,38 cachedTokens: final.usage.cache_read_input_tokens ?? 0,39 });40}What the sprint delivers.
Five deliverables. Fixed price. Readable code your team can extend after handoff.
- 01One LLM integration wired end-to-end into your existing stack
- 02Prompt caching + rate-limit handling + retry logic
- 03Streaming responses where UX benefits (chat, long-form, summaries)
- 04Token usage + cost observability hooked to your logging
- 05Readable integration code + README — your team can extend it
Fixed fee. Fixed scope. One week.
One integration per sprint. If you need more, stack sprints or book a multi-feature engagement. No retainers, no hourly meter, no surprise invoices.
- turnaround
- 1 week
- scope
- One LLM integration · caching · streaming · retries · observability · README handoff
- guarantee
- Production-grade code. Readable. Yours to extend.
API Integration Sprint vs DIY vs freelancer vs SaaS.
Four dimensions. The lime column is what you get when you buy a fixed-price sprint instead of hacking a wrapper, hiring hourly, or renting a black box.
| Dimension | DIY wrapper | Freelancer | SaaS AI vendor | Afterbuild Labs sprint |
|---|---|---|---|---|
| Approach | DIY hacky wrapper | Hiring a freelancer | SaaS AI vendor (Intercom Fin, etc.) | Afterbuild Labs API Integration Sprint |
| Price | Engineering time (hidden cost) | $4K–$15K + 3–6 weeks | $20K+/yr subscription, vendor-locked | $1,999 fixed · 1 week |
| Production features | No caching, no streaming, no retries | Usually no caching; quality depends on hire | Black box — you don't own the prompts or logic | Caching · streaming · retries · observability |
| Handoff | Tribal knowledge in one person's head | Whatever the freelancer leaves behind | Nothing — you rent, you don't own | Readable code + README · your team extends it |
Who should book the sprint (and who should skip it).
Book the sprint if…
- →You have a product and want to add one LLM-powered feature in a week.
- →You want production-grade code from day one, not a brittle wrapper you'll rewrite in month three.
- →You need caching, streaming, and observability from day one — not a 'we'll do that in v2' plan.
- →You have a deadline (launch, demo, raise) inside of three weeks.
- →You want to own the integration code and the README, not rent from a SaaS vendor.
Do not book the sprint if…
- →You need an agent with 2–3 tools and guardrails — book AI Agent MVP ($9,499) instead.
- →You need retrieval-augmented generation across your docs — book RAG Build ($6,999) instead.
- →You need n8n-style workflow automation — book AI Automation Sprint ($3,999) instead.
- →You want a plan, not implementation — book AI Cost Audit ($2,499) or AI Readiness Audit (free).
- →Your stack is exotic (mainframe, custom runtime) without HTTP access — we confirm fit on scoping call.
API Integration Sprint — your questions, answered.
Related build engagements
Stop planning. Ship the LLM integration in a week.
Seven days. $1,999 fixed. One LLM feature wired into your existing stack, production-ready from day one — prompt caching, streaming, retries, observability, handoff README. Start the sprint now.
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