AI Integration for Existing Software
LLM features inside a real backend: streaming, auth, rate limits, retries, fallback, and cost control.
How do you add an LLM feature to existing software without destabilising it?
Caffinix integrates LLM features into backends that are already in production — streaming UX, auth-aware actions, rate limits, retries, multi-provider fallback, cost controls and logging — rather than calling a model API and hoping. The work lands inside the client's existing product and permissions model and is handed over with docs and operational guardrails.
We integrate AI into the software you already run. That means more than calling a model API: streaming UX, auth-aware actions, rate limits, retries, multi-provider fallback, cost controls, logs, deployment, and a clean handover to your team.
What you get
- ✓Production LLM feature shipped inside your existing product and permissions model
- ✓Provider fallback, retry behavior, rate handling, and cost controls where needed
- ✓Backend handover with docs, environment notes, and operational guardrails
Who it's for
- —SaaS teams adding AI to an existing app without destabilizing the core product
- —Founders who need a senior backend engineer who also understands LLM behavior
- —Teams that want provider flexibility instead of wiring one model directly everywhere
How we work
A real engagement, week by week.
- 1Week 1
Architecture + risk map
- Review your app, auth model, APIs, data boundaries, and deployment path
- Decide where AI belongs in the user workflow and where it should not act
- Scope provider, gateway, fallback, logging, and cost-control requirements
- 2Week 2–3
Feature implementation
- Build the LLM flow, streaming behavior, tool calls, and backend endpoints
- Add retries, rate limits, structured outputs, and failure handling
- Instrument traces, usage, and costs so the feature can be operated
- 3Week 4+
Production release
- Test against realistic data and edge cases before rollout
- Ship behind flags or staged access when risk calls for it
- Document the integration so your engineers can maintain it
Why us
Why us.
Heimdall is our production LLM gateway for multi-provider routing, fallback, auth, rate handling, and cost control.
EvidenceVault proves the backend side: multi-tenant AI document processing with tenant isolation and deployable infrastructure.
We are strongest where AI meets backend engineering, not where a prototype ends.
Common questions
Things prospects ask first.
Yes. We usually integrate through your repo, PR flow, environment setup, and review process instead of rebuilding around you.
OpenAI, Anthropic, Gemini, and provider-agnostic gateways are all workable. The right choice depends on latency, quality, price, and data constraints.
Yes. We can implement streaming in the frontend and backend, including cancellation, partial states, and fallback behavior.
We design usage limits, logging, model routing, prompt size controls, caching where appropriate, and dashboards or reports that make spend visible.
Also booking now
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- →AI Agent Reliability & EvalsOn-call reliability, eval suites, observability, and model/cost optimization for production AI agents.
- →Fractional AI LeadA senior AI engineer and product lead on your team part-time — architecture, build oversight, and hands-on delivery, without a full-time hire.
- →AI Workflow Automationn8n, Make, or Python pipelines that replace the manual handoffs eating your team's hours — with AI steps only where they earn it.
- →AI-Powered Product & MVP BuildsWeb and mobile products with AI built in, launched in 4–8 weeks — scoped hard to the core loop, on a stack that survives past launch.
Ready to start this?
Tell us what you're trying to ship. A senior engineer replies within one business day, and the first call is 20 minutes — we'll say straight whether it's a fit.
Prefer email? contact@caffinix.com