Service
Groq and LLM Integration
We integrate language models through Groq where inference speed changes what the interface can do, with typed tools, grounded retrieval and measured evaluation.
Usage
What we build with Groq and LLM Integration
- Conversational assistants where response latency decides whether it feels usable
- Classification and routing of inbound messages and documents
- Structured extraction validated against a schema
- Retrieval grounded answering over company documents
- Drafting and summarisation inside existing tools
- Tool calling agents bound to your application functions
Rationale
Why we choose it
Latency is a product feature
A response that begins immediately reads as a conversation. The same answer after several seconds reads as a form submission. For interactive assistants that difference decides adoption.
Open models keep the exit open
Running open weight models means the same model can move to another provider or to hardware you control, which is not true of a proprietary endpoint.
Predictable cost at volume
Per token pricing on open models makes high volume classification and extraction affordable in places where a premium model would not be.
Practice
Architecture patterns we use
Grounding before generation
Retrieval runs first, filtered by the requesting user permissions, and the model answers from the returned passages with citations.
Schema validated structured output
Extraction returns JSON checked against a schema. Anything malformed is rejected and retried rather than written to a record.
Provider fallback
A secondary provider or a smaller model handles requests when the primary is slow or failing, so an outage degrades quality rather than availability.
Evaluation before deployment
A fixed set of real cases with expected outcomes is scored before any prompt or model change ships.
Budget and cache controls
Per feature spend caps, response caching for repeated work, and context truncation policies keep cost predictable.
Related work
Case studies to explore
These projects share relevant engineering concerns. Each study identifies its documented technology choices.
- AI Fitness Coaching ApplicationAn AI coaching PWA with Supabase authentication and data, Groq inference using Llama 3.3 70B, and private network deployment.
- B2B Lead Intake & CRM AutomationA completed integration for an anonymous B2B services client: website lead funnel, quote forms, CRM API ingestion, pipeline and AI automations, Resend email, and n8n orchestration.
- Digital Learning & Product PlatformAn e learning and digital product platform covering AI courses, prompts and automations, including a 15 lesson beginner course.
Build with our Groq and LLM Integration engineers
Tell us what you are building and what it has to connect to. We will come back with an approach, a scope and what it would take.
- A mutual NDA is signed before project details are shared.
- Scope is fixed in writing before any build starts.
- You own the source code, the IP and the documentation at delivery.
- Support plans are available and tailored on a discovery or technical call.