Production AI agents & LLM engineering that actually ship.
Moving past fragile toy demos requires stateful orchestration, deterministic guardrails, CI/CD evaluation harnesses, and secure GPU serving infrastructure. We engineer resilient autonomous workflows and enterprise RAG systems — vendor-neutral, fully observable, and secured against prompt injection and data exfiltration.
Select your agent use case and target infrastructure model.
Calculate your recommended orchestration framework, safety guardrails, delivery timeline, and infrastructure footprint in two clicks.
Production RAG & Knowledge Agent Platform
Permission-aware document chunking, semantic vector search with re-ranking, citation grounding, and strict data boundary enforcement to prevent data leakage.
Where an agent earns its complexity.
Internal knowledge workflows
Permission-aware retrieval and guided actions across approved internal sources, with citations and escalation paths.
Operational automation
Bounded agents that inspect systems, propose changes and execute approved steps through observable tools.
Customer-facing assistants
Support and product workflows with evaluation, data boundaries, human handoff and production monitoring.
Discover, prototype, evaluate, integrate, operate.
Discover
Define the user decision, data boundary, acceptable failure and measurable success criteria.
Prototype
Build the narrowest useful workflow with representative data and explicit human checkpoints.
Evaluate
Test task completion, groundedness, safety, latency and cost before expanding access.
Integrate & operate
Connect production tools, add observability, incident controls and a repository-owned handover.
Security and human oversight are part of the architecture.
Data and tool boundaries
Least-privilege access, scoped tools, auditable actions, protected retrieval and explicit treatment of sensitive data.
Human authority
Clear review and escalation points for decisions that are costly, irreversible, regulated or customer-impacting.
Evaluation and monitoring
Versioned test sets, quality thresholds, trace review and production alerts tied to failure modes.
When not to use an agent
If deterministic software, search or a simple workflow can solve the problem more safely, we recommend that instead.
Production AI agent architectures we design and ship.
From enterprise-grounded knowledge engines to autonomous operational agents — built with deterministic guardrails and state machine reliability.
Enterprise RAG & Hybrid Search
Multi-stage retrieval combining BM25 keyword search, dense vector embeddings, and cross-encoder re-ranking. Enforces tenant-level IAM and access control lists with zero hallucination leakage.
Stateful Multi-Step Agent Workflows
Cyclic graph orchestration using LangGraph or Temporal. Supports long-running human-in-the-loop approvals, deterministic rollback checkpoints, and fault-tolerant state recovery.
Self-Hosted vLLM & Private GPU Clusters
High-throughput, cost-efficient inference clusters running Llama 3, Mistral, and DeepSeek on dedicated Kubernetes (EKS/GKE). 4x lower latency and 100% data residency inside your VPC.
Deterministic Safety Guardrails
Dual-layer validation running input/output boundary filters via NeMo Guardrails and Pydantic schemas. Intercepts prompt injections, jailbreaks, PII leakage, and unauthorized tool invocation.
Automated CI/CD Evaluation Harnesses
Automated regression evaluation integrated into Git pull requests using Ragas and DeepEval. Quantifies answer relevancy, faithfulness, and context recall before code merges to production.
Adversarial AI Red Teaming
Comprehensive offensive testing simulating hostile inputs, indirect prompt injections, RAG poisoning, and agent tool abuse aligned to the OWASP Top 10 for LLMs.
Take AI further into production.
AI agents is the pillar. These focused paths cover the two hardest parts: shipping AI reliably, and making your own engineers faster with it.
Production AI development
Take LLM and agent apps from demo to dependable with evaluation, guardrails, observability and scalable serving.
AI developer experience
Roll out coding copilots and internal AI tooling with guardrails, measured against DORA metrics.
Ship faster
Combine platform and AI to raise deployment frequency and cut lead time without more incidents.
AI agents FAQ.
What does an AI agents consultancy do?
Design and ship production agent systems: workflows, RAG, evaluation, guardrails and the infrastructure to run them. Not slideware.
Do you lock us into a model vendor?
No. Own the platform, swap the model. Open tooling on your cluster.
Ready to move agents past the demo?
Score your readiness, or book a free 30-minute architecture call to talk through the sprint.