Workstream 01
LLM-powered features
Chat, search, summarization, extraction, and generation with robust fallback and routing strategies.
CAPABILITIES
AI-native products
LLMs, agents, RAG, and evaluation in production. We build AI features that handle real users, real data, clear safety constraints, and sustainable unit economics.
LLM-powered features
RAG systems
Agents and tool use
Capability focus areas
Foundation modelsFrameworksVector storesServing
What we do
Workstream 02
RAG systems
Ingestion, chunking, embeddings, hybrid retrieval, and re-ranking focused on answer quality first.
Workstream 03
Agents and tool use
Task-completion agents with deterministic tool execution, memory controls, and timeout resilience.
Workstream 04
Evaluation and observability
Golden datasets, eval harnesses, cost and latency telemetry, and prompt/version governance.
Workstream 05
Fine-tuning and routing
LoRA adaptation and multi-model routing when generic models fail cost or domain-accuracy goals.
Workstream 06
Guardrails and safety
Input and output controls, PII handling, and layered mitigation against prompt abuse.
Workstream 07
Production governance
Model/prompt versioning, release gates, and incident playbooks so AI behavior is observable and auditable over time.
How we work
01
Use-case and eval definition
Define success metrics and build an evaluation baseline before prompt iteration begins.
02
Core feature implementation
Ship retrieval, generation, and orchestration layers with observability from day one.
03
Quality and cost optimization
Improve pass rates and response economics through targeted routing and retrieval tuning.
04
Production hardening
Add guardrails, monitoring, and release workflows to support safe ongoing iteration.
Tech we use
Foundation models
OpenAIAnthropicGoogleLlamaMistralQwen
Frameworks
LangChainLlamaIndexHaystackDSPyDirect SDKs
Vector stores
pgvectorPineconeWeaviateQdrantVespa
Serving
vLLMTGIOllama
Evaluation
RagasPromptfooCustom eval harnesses
Observability
LangfuseHeliconeCustom telemetry
Featured case study
Proof in production
We built a RAG system over 400k regulatory documents and improved answer accuracy from 68% to 94%.
Read case studyQuestions we get
GPT-4, Claude, or open-source?
Often a mix. We route by task requirements, latency, cost, and privacy constraints.
Do we need a dedicated vector database?
Not always. pgvector covers many use cases; dedicated stores are introduced when scale or recall needs demand it.
Is fine-tuning worth it?
Sometimes. Prompting and retrieval solve most issues first; fine-tuning helps with domain fit and cost.
How do you reduce hallucinations?
We combine grounded retrieval, citation requirements, structured outputs, and eval-gated releases.
Ready to ship AI that works in production?
We can scope your first production-grade AI feature and define the right quality guardrails.
Build with AI