AI Integration
AI that survives production.
Not just demos.
LLM-powered features, RAG pipelines, and autonomous agents woven natively into your product — with evaluation harnesses, cost guardrails, and monitoring from day one.
Prompt engineering, streaming, fallback logic — AI that behaves in production like it does in demos.
RAGAS, LLM-as-judge, regression suites — you see the metrics, not just the output.
Token budgets, caching, model selection — predictable spend at any scale.
Capabilities
Everything we do
in ai integration.
LLM Feature Design
We design LLM-powered features that are useful, reliable, and cost-effective. Prompt engineering, system prompts, and context window management — not guesswork.
- System prompt design with versioning and A/B testing infrastructure
- Context window management — chunking, summarisation, and priority-based inclusion
- Streaming responses with partial rendering for perceived performance
- Fallback chains: primary model → fallback model → graceful degradation
RAG Pipelines
Retrieval-augmented generation that actually retrieves the right context. Chunking strategy, embedding model selection, and retrieval evaluation with RAGAS.
- Document ingestion pipelines with semantic chunking and metadata extraction
- Embedding model benchmarking — we test multiple models on your data before choosing
- Vector database setup (Pinecone, Weaviate, pgvector) with hybrid search
- Retrieval quality evaluation using RAGAS metrics: faithfulness, relevance, recall
AI Agents
Multi-step agents that use tools, maintain memory, and know when to ask for help. LangGraph orchestration with human-in-the-loop controls.
- LangGraph for stateful, multi-step agent orchestration with checkpoints
- Tool use design — giving agents the right tools with proper guardrails
- Memory management: short-term conversation context + long-term knowledge
- Human-in-the-loop approval flows for high-stakes decisions
Evaluation & Testing
You can't improve what you can't measure. We build evaluation harnesses before building features — so you know if the AI is actually working.
- Automated regression harnesses that run on every deployment
- LLM-as-judge evaluation for subjective quality metrics
- Output monitoring dashboards with drift detection and alerting
- Golden dataset curation for consistent benchmarking over time
Cost & Performance
AI features that scale without surprising you with the bill. Token budgets, semantic caching, and model routing that balances quality and cost.
- Token budget management with per-request and per-user caps
- Semantic caching — identical or similar queries served from cache
- Model routing: simple queries → small model, complex → large model
- Latency optimisation: streaming, parallel tool calls, batch processing
Tech Stack
Every tool we use
to deliver ai integration.
LLM Providers
Frameworks
Vector & Data
Tooling
Process
How we deliver
ai integration.
What to expect from week one to launch — and beyond.
Feasibility & Architecture
We audit your data, model options, and latency requirements. We also define the evaluation harness before building anything.
Prototype & Evaluate
A working RAG or agent prototype with baseline metrics in week two. No black-box demos — you see the evals.
Production Hardening
Streaming, error handling, cost monitoring, fallback logic. AI features that behave in production like they do in demos.
Monitoring & Iteration
Post-launch LLM output monitoring, automated regression testing, and a monthly model review.
Case studies
Work that proves it.
“The AI integration became our core differentiator. Competitors are still catching up. Averon didn't just build the feature — they built the evaluation harness that lets us improve it every month.”
TNTom Nielsen
CTO, Vault AI (Series A)
FAQ
Common questions about
ai integration.
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