AI & Data
LLM Integration
Production LLM features inside your product—prompting, routing, caching, and cost/latency controls.
LLM Integration puts model calls behind clean application interfaces. We design prompts and structured outputs, add caching and fallback models where useful, and instrument cost and latency so AI features remain operable as usage grows.
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Who it’s for
- • Product engineering teams adding AI features
- • Platforms needing multi-model routing
- • Teams replacing prototype notebooks with production paths
Problems we address
- • Model calls are scattered through the codebase
- • Costs and latency are invisible until the bill arrives
- • Prompt changes have no review or rollback story
Expected outcomes
- • Centralized LLM client with structured interfaces
- • Observability for tokens, latency, and errors
- • Prompt versioning and safe rollout patterns
Capabilities
Concrete engineering capabilities included in a typical engagement for this service.
Provider SDK integration
Structured output and validation
Prompt templates and versioning
Caching and rate-limit handling
Model routing and fallbacks
Safety filters appropriate to the use case
Technology
Representative technologies used for this service. Final stack depends on your estate.
- OpenAI
- Anthropic
- Open-weight model APIs
- Node.js / Python
- Redis caching
- OpenTelemetry / logging
Architecture
AI application flow
User requests through the application into model APIs, tools, and storage.
Deliverables
- • LLM integration module in your stack
- • Prompt and config documentation
- • Observability dashboards or log fields
- • Operational runbook for provider incidents
Out of scope
- • Training foundation models from scratch
- • Provider account billing guarantees
Timeline
Typical timeline
2–5 weeks
Timeline depends on scope, access, and dependencies—not a delivery guarantee.
Process
A clear delivery path from discovery through handover and optional support.
01
Discovery
Goals, constraints, success criteria, and current-state review.
02
Architecture
Target design, interfaces, risks, and delivery sequence.
03
Implementation
Incremental build with visible progress and documented decisions.
04
Testing
Functional checks, failure paths, and acceptance criteria validation.
05
Deployment
Controlled release to staging and production with rollback paths.
06
Handover
Runbooks, access notes, and operator/admin walkthrough.
07
Support
Optional hypercare window or retainer continuity after go-live.
Custom engagement
Pricing depends on architecture, traffic profile, and integration depth. Share your requirements for a scoped quote.
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FAQ
We integrate the providers and hosting options you choose—managed APIs or self-hosted endpoints you operate.
Ready to build?
Tell us about your environment, constraints, and target outcomes. We’ll recommend a package or a scoped quote.