Agentic AI and LLM systems that automate your highest-cost workflows.
We build agentic AI systems and AI agents that automate your workflows — cost-optimized inference, multi-agent orchestration, and reliable semantic search at scale. Most clients see 60–80% inference cost reduction within the first sprint.
Agentic AI & Multi-Agent Orchestration
- LangGraph workflow design for complex, stateful agent pipelines
- Tool-calling agents with memory, retry logic, and fallback routing
- Multi-agent coordination with task decomposition and parallel execution
- Human-in-the-loop approval flows for high-stakes decisions
Inference Cost Optimization
- QLoRA quantization for 4-bit and 8-bit model compression
- Model routing: cheapest capable model per task class
- Prompt caching and batch inference for throughput gains
- AWS Bedrock on-demand and provisioned throughput management
RAG & Semantic Search
- pgvector and Pinecone vector store integration
- Hybrid BM25 + dense retrieval for enterprise document search
- Chunking strategies optimized for long-context faithfulness
- Re-ranking pipelines for precision-critical retrieval
Technologies we ship
LangChainLangGraphAWS BedrockOpenAIAnthropic ClaudePostgreSQL + pgvectorPineconePythonFastAPIDocker
Typical Agency vs. VeloceTech
| Category | Typical Agency | VeloceTech |
|---|---|---|
| Model Selection | Default GPT-4 for every task regardless of cost | Per-task model routing — cheapest capable model per job class |
| Inference Cost | Uncontrolled, grows with usage | Quantized, cached, and batched — up to 80% cost reduction |
| Reliability | Single model, no fallback | Retry logic, circuit breakers, and failover routing |
| Observability | Logs only in production | Trace-level LLM observability with LangSmith or equivalent |
| Fine-tuning | Off-the-shelf prompts only | QLoRA fine-tuning on proprietary datasets for domain-specific accuracy |