Services / 04

Hire an LLM integration developer for ChatGPT and Claude in production.

LLM integration services for SaaS products and internal operations. Ship Claude, GPT-4 or Gemini into your existing application with streaming, cost controls, prompt engineering, guardrails and evaluation — the things that separate a demo from a production system.

§ 01

What you get

Multi-provider routing

Claude for hard reasoning, GPT-4o for multimodal, Gemini Flash for cheap bulk. One integration, routed automatically to the right model per task.

Streaming UX

SSE or WebSocket streaming so users see tokens as they generate. Stop/retry controls. Works on slow networks.

Cost controls

Prompt prefix caching, semantic caching, token budgets, per-user rate limits. Cost dashboards from day one.

Security & guardrails

Prompt injection defence, PII redaction, output filtering, tool-use sandboxing. Safe for production traffic.

Evaluation harness

Golden test sets, regression tests on every deploy, automated scoring. Prompts behave like code — versioned, tested, reviewed.

Self-hosted option

Llama 3.3, Mistral, Qwen on Ollama/vLLM. For regulated data or high-volume cost reasons.

§ 02

How I build it

  1. 01
    Scope
    What feature, what latency, what cost ceiling, what data sensitivity. Output: an architecture one-pager and cost projection.
  2. 02
    Prompt design
    Prompt templates with examples, system instructions, tool specs. Versioned in git like code.
  3. 03
    Integration
    Wired into your product with streaming, auth, rate limits, retries. Works against your real data and users.
  4. 04
    Evaluation
    Golden set with expected behaviours. Regression runs on every deploy. Online feedback loop for continuous improvement.
  5. 05
    Ship + monitor
    Dashboards for latency, cost, quality, errors. Oncall playbooks. Monthly tune-up retainer available.
§ 03

Stack used

LLM providers

Anthropic Claude · OpenAI GPT-4 / GPT-4o · Google Gemini · Groq · Together · Fireworks · Ollama (self-hosted)

Caching & routing

Anthropic prompt cache · Redis semantic cache · Portkey gateway · LiteLLM · custom routers

Streaming

Server-Sent Events · WebSockets · Vercel AI SDK · AsyncIterable streams

Eval & observability

LangSmith · LangFuse · Helicone · Braintrust · custom tracing

Frontend

React · Next.js 15 · Vercel AI SDK UI · shadcn/ui · Tailwind

Backend

FastAPI · Node.js · Express · NestJS · Hono · Cloudflare Workers

§ 05

Engagements

Single feature

$1,500 – $4,000

One LLM-powered feature integrated into your product with streaming, auth, rate limits. 1 week.

Multi-feature

$6,000 – $15,000

Several LLM features, routing, caching, evaluation harness, dashboards. 2–4 weeks.

Platform

$20,000+

LLM integration layer across multiple products or teams with shared infra. 6+ weeks.

Fixed-price or monthly retainer. NDA and IP-assignment standard. Hourly available on request.

§ 06

Frequently asked

What does LLM integration for business mean?

LLM integration is the work of wiring large language models (Claude, GPT-4, Gemini) into your existing product or internal operations — authentication, rate limits, streaming, cost controls, prompt engineering, guardrails, evaluation, monitoring. It’s the gap between "I tried Claude in a playground" and "Claude runs our customer support at 5,000 conversations a day."

Should I use Claude, GPT-4 or Gemini?

Depends on the task. Claude Sonnet/Opus for reasoning-heavy and long-context work. GPT-4o for balanced general-purpose and cheaper multimodal. Gemini 2.0 Flash for cost-sensitive high-volume. I often route between them based on task and cost. I’ve shipped production systems on all three.

How do you control LLM costs in production?

Caching at multiple layers (prompt prefix cache, semantic cache for similar queries), model routing (cheap model for easy tasks, expensive model for hard ones), token budgets, truncation, and per-user rate limits. Cost dashboards from day one so the bill never surprises anyone.

How do you prevent prompt injection attacks?

Treat every user input as untrusted. Separate system instructions from user content clearly. Sanitise tool arguments with schemas. Use a classifier to flag injection attempts. For high-stakes actions, require human confirmation. Never have the LLM execute code from user input directly.

Can you integrate Claude into my existing SaaS / CRM / helpdesk?

Yes. Typical integrations include Intercom, Zendesk, HubSpot, Salesforce, Slack, Notion, Linear, Jira, custom internal tools via REST. Streaming responses to your UI, tool use for reads/writes, audit logs for every action.

How do you handle streaming and real-time UX?

Server-Sent Events (SSE) or WebSockets to stream tokens as they generate. Graceful degradation for slow networks. Stop/retry controls for the user. I ship streaming by default — non-streaming feels broken in 2026.

Do you build self-hosted LLM integrations?

Yes. Llama 3.3, Mistral, Qwen via Ollama or vLLM. Deployed on GPU instances (AWS g6, GCP A100) or Groq/Together inference endpoints. Right choice for regulated industries or high-volume cost optimisation.

How do you evaluate LLM integrations in production?

Offline: golden dataset with expected behaviours, run on every deploy. Online: sampled human review, automated LLM-as-judge scoring, user feedback loops. Every integration I ship has regression tests for the prompts.

Ready to build?

Same-week start. Email reply within 24 hours. Written enquiries welcome.