🧠 AI Integration · Updated May 2026

Production-ready AI features for your SaaS.
Claude. OpenAI. Streaming. Cached. Guarded.

We add Claude or OpenAI features to your existing app with the boring parts done right: prompt caching, per-user spend caps, streaming responses, prompt injection protection, and graceful fallback when the provider has an outage. From $2,500 fixed-price.

Scope my AI feature (48h) →Or: full SaaS MVP with AI built-in →

From $2,500 fixed-price · Cost guardrails included · 2-4 week delivery · Anthropic + OpenAI + Vercel AI Gateway

Quick Answer: In 30 seconds

We add production-ready Claude or OpenAI features to your existing SaaS for $2,500-12,000 fixed-price. Scope includes prompt caching (90% cost reduction), per-user spend caps, streaming UI, prompt injection protection, and cost monitoring alerts. Delivery 2-4 weeks. As of May 2026 we have 2 slots open this month.

The "AI features in production" problem

We see the same 5 failure modes on every founder's AI integration that we are asked to clean up:

  1. No prompt caching. Sending the same 8,000-token system prompt with every request costs ~10x what it should. Anthropic and OpenAI both support prompt caching that cuts repeated context costs by 90%. Most integrations we see have it off because the docs are not obvious.
  2. No per-user spend caps. A single bad actor (or a single buggy frontend loop) can rack up $1,000-10,000 in API costs before you notice. We add per-user daily and monthly caps stored in your database, checked before every call.
  3. No streaming. Users wait 30 seconds for a response that could have been streaming character-by-character starting at 500ms. They bounce. We use the Vercel AI SDK to make streaming a one-liner.
  4. No fallback. When Anthropic has a 30-minute outage (it happens), your AI feature is dead. We use Vercel AI Gateway or custom router code to automatically fall back to a secondary provider (OpenAI or Google) without your users noticing.
  5. No prompt injection defense. User-controlled text gets concatenated directly into the system prompt, and an attacker extracts your hidden instructions or executes unintended actions. We use isolation patterns and output allowlists.

What you get

  • The AI feature, working. Chat, generation, summarization, classification, RAG over your data — whatever you scoped.
  • Prompt caching configured for Anthropic and OpenAI. Documented in your runbook.
  • Per-user spend caps stored in your database, enforced on every call. Configurable thresholds.
  • Streaming UI using Vercel AI SDK so responses build live as users watch.
  • Cost monitoring with daily and monthly alerts wired to your email (optionally Telegram).
  • Provider fallback via Vercel AI Gateway or custom router so an outage on one provider does not kill the feature.
  • Prompt injection defense via input isolation and output allowlists for any action with side effects.
  • Audit logs of every request and response, retained 30 days, so you can debug bad outputs.

Pricing

  • Single chat feature — $2,500-4,000. Conversation history, streaming UI, cost controls. 1-2 weeks.
  • RAG over your data — $5,000-8,000. Embeddings + vector search (Supabase pgvector or Pinecone) + retrieval prompt. 2-3 weeks.
  • Multi-step agent — $8,000-12,000. Tool calling, workflow orchestration, human-in-the-loop confirmations. 3-4 weeks.
  • Maintenance — $750/month. Prompt iteration, cost optimization, model upgrades when new ones ship. Cancel anytime.

Which provider should you use?

We default to Claude (specifically Claude 4.7 Opus for reasoning-heavy workloads, Sonnet 4.6 for general, Haiku 4.5 for cheap/fast) because the output quality on coding and reasoning tasks is meaningfully better in our experience. OpenAI is still the right choice for image generation (DALL-E, gpt-image-1) and for cases where you need the o-series reasoning models specifically.

For multi-provider deployments we use Vercel AI Gateway — single API key, model-agnostic strings like"anthropic/claude-opus-4-7", automatic failover, unified observability. Zero data retention by default. For most founders this is the right starting point, even if you only use one provider initially.

When NOT to hire us

  • You have not actually tried the AI feature manually yet (just paste examples into the Claude or ChatGPT web UI). 30 minutes of manual testing tells you if the feature even works before you spend $5,000 building it.
  • You want AI to "automate your business" without specifying what task. Scope it first — "summarize support tickets into a daily digest" is buildable; "AI for everything" is not.
  • You expect the AI to be perfect. It will not be. Plan for 90-95% accuracy and have a path for the other 5-10% (human review, retry, error message).

Frequently Asked Questions

Which AI providers do you integrate with?
Primary: Anthropic Claude (Claude 4.7 Opus, 4.6 Sonnet, 4.5 Haiku) and OpenAI (GPT-4, GPT-4o, o-series). Also: Google Gemini, Mistral, DeepSeek, and Vercel AI Gateway for unified multi-provider routing with automatic failover. We usually recommend Claude for reasoning/coding workloads and OpenAI for image generation, but the right choice depends on your specific use case and budget. We will benchmark both during scoping.
What does "production-ready" mean for AI features?
It means: (1) prompt caching enabled so repeated context costs 90% less, (2) per-user rate limits so a single user cannot rack up a $5,000 bill, (3) cost monitoring with daily/monthly alerts and hard caps, (4) streaming responses so users see output as it generates instead of waiting 30 seconds, (5) graceful fallback if the AI provider has an outage, (6) prompt injection protection on any user-controlled input, (7) audit logs so you can debug bad outputs after the fact. Most "AI features" we see in the wild miss 4-5 of these.
How much does it cost?
Fixed-price quotes from $2,500-12,000 depending on scope. Single chat feature with conversation history = $2,500-4,000 (1-2 weeks). RAG (retrieval-augmented generation) over your existing data with embeddings + vector search = $5,000-8,000 (2-3 weeks). Multi-step agent with tool calling + workflow orchestration = $8,000-12,000 (3-4 weeks). As of May 2026 we have 2 AI integration slots open this month.
How do you handle API costs and prevent runaway spend?
Three layers. (1) Per-request hard cap on output tokens so a single response cannot exceed a budget. (2) Per-user daily and monthly spend caps stored in your database, checked before every API call. (3) Account-level monthly alerts wired to your email and (optionally) Telegram. We also enable prompt caching by default on Anthropic and structured input caching on OpenAI — this alone typically cuts your bill by 60-90% for chat-style applications. We document all this in your runbook so your future self can adjust thresholds.
Can you integrate AI into an app I already built? Or only new builds?
Both, but integration into an existing app is the more common engagement. You give us read access to your repo, we scope a feature against your existing data and auth model, and ship the integration as a series of PRs you review and merge. We do not rebuild your app — we add the AI surface area without touching the parts that work. Typical existing-app integration: 2-3 weeks, $4,000-8,000.
What about RAG — when do I actually need it?
You need RAG when the AI needs to answer questions about your specific data (user uploaded documents, your knowledge base, your product catalog, your code). You do NOT need RAG when the AI just needs to generate text in a specific style or perform a defined task. We often see founders over-engineer to RAG when a well-written system prompt would solve their problem at 1% of the cost. We will tell you honestly during scoping.
Will the AI feature be fast enough? My users hate waiting.
Streaming is the answer. Modern AI APIs let you stream tokens as they generate, so users see the response building character-by-character within 500ms of submitting. We use the Vercel AI SDK (free, well-documented) to make this trivial. We also use Claude Haiku or GPT-4o-mini for sub-second responses on routing/classification tasks, and reserve the bigger models for the actual generation step.
What about prompt injection and AI safety?
Three layers. (1) System prompts are isolated from user input — we never concatenate user text directly into instructions. (2) Output is checked against allowlists for actions that have side effects (sending email, calling APIs, mutating data). (3) Sensitive operations require human-in-the-loop confirmation even when the AI suggests them. We do not promise the AI cannot be tricked — anyone who promises that is lying — but we promise the damage radius is limited to what your business logic allows.

Ready to add AI to your SaaS?

Send us a one-paragraph description of the feature and a link to your existing app. You get a scoped quote with a recommended provider, model, and architecture within 48 hours.

Get my 48-hour quote →