LLM Integration
Put large language models behind a real application layer
We integrate LLMs into products with retrieval, tool use, logging, evaluation and cost controls — so the model is infrastructure, not a one-off script.
Definition. LLM integration is the engineering required to call large language models from an application safely: authentication, prompt and context assembly, retrieval, output validation, observability and vendor abstraction.
A direct API key in the frontend is not an LLM architecture
Production use needs a backend, secrets handling, rate limits, tracing and a plan for model changes.
Prompt sprawl
Prompts living in random files become untestable and impossible to own.
Uncontrolled spend
Without routing, caching and caps, LLM features can become an unpredictable operating cost.
Silent quality drift
Model or data changes can degrade answers with no one noticing until customers complain.
Capabilities
What we build
Specific delivery, not a vague capability list.
Model gateway
A server-side layer for keys, routing, retries and usage accounting.
Context assembly
Retrieval, memory limits and structured context so the model sees what it should — and nothing else.
Evaluation and tracing
Offline tests plus production traces so quality has an owner.
Solutions
Common implementation scenarios
Product copilots
In-app assistance that uses tenant data through your existing permission model.
Internal LLM access
A governed way for staff tools to call models without spreading API keys.
Replacement of brittle scripts
Move ad-hoc prompt scripts into a maintainable service with logging.
How we work
Development process
01
Discovery
We clarify the business problem, current systems, constraints, success metrics and decision-makers before recommending a build.
02
Strategy
We define the product scope, delivery sequence and commercial trade-offs so engineering work maps to a measurable outcome.
03
Architecture
We design the application, data, integration and security model before implementation starts, reducing rework later.
04
UI/UX
We design the interfaces operators and customers will actually use — with clarity, accessibility and conversion in mind.
05
Development
We implement in short, reviewable increments with staging environments and visible progress against the agreed scope.
06
QA
We test functional flows, edge cases, integrations, performance and release readiness before anything reaches production.
07
Launch
We ship with a controlled cutover, monitoring and a clear rollback plan so go-live is an operational event, not a gamble.
08
Support & Optimization
After launch we stabilize, measure and improve — fixing issues quickly and iterating on the features that affect the business.
Stack
Technologies
- OpenAI
- LLMs
- AI APIs
- Vector databases
- Node.js
- AWS
Industries
Where this service is typically used
Ecommerce
We help ecommerce businesses build Shopify experiences, operational AI, mobile clients and the integrations that keep catalog, orders and inventory honest.
SaaS
We help software companies ship AI capabilities, integration surfaces and mobile clients that respect tenancy, metering and a roadmap you can maintain.
Engagement
How we work commercially
Fixed scope
A defined outcome, timeline and budget after discovery. Best when requirements are clear enough to estimate with confidence.
Dedicated team
A stable product squad that works as an extension of your team across a roadmap, not a one-off ticket list.
Ongoing development
Retained engineering capacity for iteration, integrations, maintenance and new capabilities after the first release.
Consulting
Architecture, vendor selection, AI feasibility, Shopify Plus planning or integration design without a full build engagement.
FAQ
Questions about llm integration
It is the application engineering that sits between your product and a large language model: security, context, evaluation, cost and change management.
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Design an LLM integration
Share the application and the job the model should do. We will outline the gateway, data access and evaluation needed to ship it.