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ChatGPT & OpenAI Integration

ChatGPT in your
product. Done properly.

We integrate GPT-4 and the OpenAI API with real infrastructure: conversation memory, function calling, streaming, cost controls, and abuse prevention. Not just a wrapper.

Start your integration
Your product - AI assistant

Summarize last quarter's support tickets by category.

Found 1,247 tickets. Top categories: Billing (34%), Onboarding (28%), Bug reports (19%), Feature requests (14%), Other (5%). Billing tickets up 12% vs Q2 - mostly related to the pricing change on Sept 1.

Which billing issues can we close automatically?

82 tickets match auto-resolvable patterns: duplicate charges (31), expired card prompts (29), plan confusion after migration (22). Drafting responses now...

Ask anything...
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What you can build

AI Customer Support

Deflect 60–80% of tier-1 tickets without human escalation. Trained on your docs, policies, and past conversations.

Used by: SaaS companies, marketplaces, fintech apps
In-App Writing Assistant

Ghostwriting, tone adjustment, grammar, and content expansion - embedded in your editor, not a separate tool.

Used by: B2B SaaS, content platforms, CRMs
Code Generation Tool

Natural language to code, code review, documentation, and refactoring suggestions - tuned to your codebase.

Used by: developer tools, IDE plugins, internal tooling
Data Analysis Chat

Ask questions against your database or CSV files in plain English. Returns charts, summaries, and SQL.

Used by: analytics dashboards, BI tools, ops teams

Function calling & tools

Function calling lets GPT decide when to call your APIs - and what to do with the result. This is what makes AI useful, not just impressive.

CRM & database queries

"Show me all deals closed this month" → GPT calls your CRM API, retrieves structured data, formats the response in natural language.

→ GET /api/deals?closed_after=2024-01-01
Calendar & scheduling

"Book a 30-min call with Alice next Tuesday" → GPT checks availability, creates the event, sends the invite - all in one message.

→ POST /calendar/events with parsed slots
Document & file actions

"Generate a PDF summary of this contract" → GPT reads the doc, summarizes key clauses, calls your PDF API, and returns a download link.

→ POST /docs/generate with summary content

The integration layer we build

The OpenAI API is a starting point. What you need in production is a lot more.

Prompt management

Versioned prompt templates with A/B testing and rollback. No hard-coded strings in your codebase.

Conversation memory

Session persistence, long-term user memory, and context window management for multi-turn conversations.

Streaming responses

Token-by-token streaming over SSE or WebSocket so users see output immediately, not after a 5s wait.

Rate limiting & fallbacks

Per-user and per-org rate limits. Graceful fallback to smaller models or cached responses under load.

Cost monitoring

Per-request token tracking with real-time spend dashboards. Alerts before you hit budget thresholds.

Abuse prevention

Input/output content moderation, PII detection, jailbreak resistance, and audit logging for compliance.

Conversation memory patterns

In-context window
When to use

Short sessions, <15 turns, no persistence needed

How it works

Entire conversation included in every prompt. Simple, zero infrastructure. Costs scale with conversation length.

Limitations

~8k–128k tokens depending on model. Expensive for long sessions.

External vector memory
When to use

Long-running sessions, returning users, personalization

How it works

Conversation chunks embedded and stored in a vector DB. Relevant memories retrieved per turn.

Limitations

Retrieval quality depends on embedding and chunking strategy.

Hybrid approach
When to use

Production apps with >10k daily active users

How it works

Recent messages in context, older history in vector store. Summary compression for long threads.

Limitations

Most complex to build but best quality/cost ratio at scale.

Pricing & cost management

LLM costs can 10× overnight if you're not watching. We build cost controls into every integration from day one.

What drives costs up
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Long system prompts sent on every request

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No response caching for repeated queries

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Using GPT-4 for tasks that GPT-4o-mini handles

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No per-user quotas - one user can drain the budget

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Streaming responses that re-call on every keystroke

What we do to prevent it
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Prompt caching with semantic deduplication

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Model routing: cheap models for simple tasks

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Per-user and per-org daily token budgets

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Response caching for identical or near-identical queries

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Spend dashboards with Slack alerts before thresholds

Typical delivery timeline

From zero to a production ChatGPT integration in 4–6 weeks.

Week 1–2
First working feature

Scoped integration with streaming, basic prompt management, and working UI. Deployable to staging.

Week 3–4
Memory & context

Conversation memory, session persistence, function calling connected to your first data source.

Week 5–6
Production hardening

Rate limiting, cost monitoring, abuse prevention, observability dashboards. Ready for real users.

Integration benchmarks
< 200ms

Additional latency overhead from our integration layer vs. calling OpenAI directly

99.7%

API uptime with fallback model routing and graceful degradation enabled

40%

Reduction in token costs through prompt caching and semantic response deduplication

6 wks

From kickoff to a production-ready ChatGPT integration with full observability

Technology

What we build with

LLM providers
OpenAI GPT-4oGPT-4o-miniGPT-4 Turboo1Assistants APIBatch API
Streaming & transport
Server-Sent EventsWebSocketsHTTP streamingEdge FunctionsVercel AI SDK
Memory & context
pgvectorPineconeWeaviateRedis (session)Supabase VectorLangChain memory
Observability & cost
LangSmithHeliconeOpenMeterDatadog LLMCustom dashboardsSlack alerts
Safety & moderation
OpenAI Moderation APIPII detectionPrompt injection guardsRate limitingAudit logs
Case study

In-app writing assistant for a B2B CRM

The problem

Sales reps were spending 35 minutes per deal writing follow-up emails, proposals, and internal call summaries. The team needed AI writing assistance embedded directly in the CRM - not a tab-switch to ChatGPT.

What we built

GPT-4o integration with function calling to pull deal context (stage, notes, contact history) into every prompt. Streaming output, tone controls, and a prompt management layer for sales ops to iterate without engineering.

Outcomes
35min → 4min
Email drafting time
87%
Reps use it weekly
22%
Deal close rate lift
0.008
Avg resource per email
Common questions

FAQ

Do you only work with OpenAI, or other LLM providers too?

We specialize in OpenAI and the ChatGPT family, but we also integrate Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral) when they're the better fit. Most production apps we build include model routing - cheaper/faster models for simple tasks, GPT-4 for complex ones.

How do you handle rate limits and API outages?

Our integration layer includes exponential backoff, per-user rate limiting, and model fallbacks. If GPT-4 is unavailable or slow, we can fall back to GPT-4o-mini automatically. For high-volume apps, we also implement request queuing so bursts don't trigger 429 errors.

What's the risk of users manipulating the AI (prompt injection)?

Real risk for any user-facing LLM feature. We implement layered defenses: input sanitization, output validation, system prompt hardening, and OpenAI's Moderation API for content filtering. For enterprise apps, we add audit logging so every input/output is reviewable.

How do we keep our data from being used to train OpenAI's models?

OpenAI's API (unlike the consumer ChatGPT interface) doesn't use your data for training by default. We configure Zero Data Retention (ZDR) where applicable, add contractual safeguards in the API agreement, and avoid sending PII in prompts where possible - using user IDs and anonymized tokens instead.

Can you integrate with our existing tech stack?

Yes. We've integrated OpenAI with Next.js, React, Vue, Node, Python (FastAPI/Django), Rails, and .NET backends. The integration layer we build is stack-agnostic and connects over your existing auth, database, and deployment infrastructure.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in TouchSee Our Work

What happens next

1

Send us a message

Tell us what you're building or what's broken.

2

Discovery call (30 min)

We ask hard questions. You get honest answers.

3

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Contact Us

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

  • Agentic AI development and multi-agent systems
  • Generative AI consulting and LLM integration
  • RAG development and custom model deployment
  • Data engineering, MLOps and custom software
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