AI StudioAgentic AI • RAG • LLM Ops

Ship agentic AI products that stay grounded, observable, and safe.

We design and build LLM, RAG, and multi-agent systems with production-grade guardrails—covering data pipelines, evaluation, and human-in-the-loop controls so you can move fast without surprises.

Latency budget

< 1.2sP95 for RAG answers

Safety coverage

15+red-team suites & filters

Ops ready

24/7monitoring & runbooks

What we ship

Agentic AI, RAG, and LLM platforms built for production.

We architect the stack end to end: ingestion and embeddings, retrieval, reasoning, safety, evaluation, and the observability loop that keeps models honest.

Agentic AI systems

Tool-using agents that coordinate workflows, route intent, and keep humans-in-the-loop for approvals.

Action orchestrationMulti-agent routingHuman handoff

Retrieval-Augmented Generation

Grounded answers with hybrid search, vector stores, relevance tuning, and prompt-safe context windows.

Chunking + embeddingsHybrid search & rerankHallucination controls

LLM evaluation & guardrails

Offline + online evals, toxicity/redaction filters, jailbreak defenses, and policy-as-code for AI safety.

Golden sets & adversarialPII/toxicity filtersPolicy as code

AI platform & MLOps

Feature stores, prompt/version management, CI for prompts, telemetry, and rollout gates with canaries.

Prompt CI/CDObservability & tracingCost/latency tuning

Data pipelines for LLMs

Ingestion, cleaning, redaction, and quality scores to keep your knowledge base fresh for RAG + agents.

Syncs & CDCQuality scoringVersioned corpora

AI product strategy

Discovery sprints to validate use cases, success metrics, and readiness—before writing the first prompt.

Opportunity mappingKPIs & SLAsRisk/ROI model

Reference blueprints

Patterns we deploy for agentic AI, RAG, and LLM safety.

Battle-tested architectures with observability, evals, and governance baked in—adapted to your stack (OpenAI, Anthropic, Vertex, Bedrock, local models).

Pattern 1

Agentic runbooks

Task planners call domain tools (APIs, SQL, SaaS) with execution memory, safety checks, and live dashboards for ops teams.

Toolformer-styleGuarded actionsCost capsPagerDuty/Slack

Pattern 2

RAG for support & docs

Hybrid retrieval (BM25 + vectors), metadata filters, reranking, and answer assembly with citations so agents stay grounded.

Chunking + rerankCitationsFreshness SLAsVector + keyword

Pattern 3

Safety & evaluation mesh

Automated eval harness with golden sets, red-team suites, prompt regression tests, and real-time toxicity/PII filters.

Offline + online evalsJailbreak firewallsPrompt diffingScorecards

Pattern 4

Observability loop

Tracing, embeddings-level metrics, latency heatmaps, and feedback collection to continuously improve the stack.

LLM tracesHuman feedbackLatency budgetsAuto-tune

Delivery tracks

A playbook that balances velocity, governance, and trust.

We keep security, product, and data teams in the loop—every step ships with evals, observability, and human controls by default.

Week 1–2

Discovery & architecture

Use-case canvas and risk map
Reference architecture for agents + RAG
Data readiness + eval plan

Week 3–5

Prototype & evaluate

Pilot agents/RAG flows with guardrails
Offline evals + golden datasets
Latency & cost tuning

Week 6–8

Harden & launch

Observability, alerts, and SLOs
Human-in-the-loop + approval gates
Rollout playbook & training

Let's build

Ready to launch an agentic AI or RAG experience?

We can start with a discovery sprint or harden the stack you already have—evals, guardrails, and observability included.