SERVICE
AI Model Integration
Bring LLMs and ML into your product
The service
Overview
We integrate large language models, embedding search, and vision models into your existing apps — with reliable prompt chains, retrieval (RAG), guardrails, and evaluation so the AI behaves predictably in production rather than hallucinating.
What you get
- LLM integration (OpenAI, Anthropic, or open-source)
- Retrieval-Augmented Generation (RAG) pipelines
- Vector database setup (Pinecone, pgvector, Qdrant)
- Prompt engineering and evaluation harness
- Streaming responses and function calling
- Usage monitoring, logging, and cost controls
- Fallback and moderation guardrails
- 30 days of post-launch support
Typical use cases
Production AI integration
Useful intelligence with guardrails
An AI feature needs more than a model call. We design the data path, evaluation, safety checks, and operating controls around the user task.
- 01
Choose the right task
Define the user workflow, source material, model constraints, and what a useful answer or action should look like.
- 02
Connect models and context
Integrate hosted or open models with structured prompts, retrieval where useful, and the product’s existing data sources.
- 03
Evaluate and protect
Test representative inputs, add moderation and fallback behavior, and track quality, latency, and usage costs.
- 04
Ship inside the product
Deliver the user experience, monitoring, and deployment path so the feature can be maintained as models and needs change.