Enterprise AIOps · Engineering for AI at scale

AI that runs in production.

End-to-end AIOps for engineering teams shipping AI at scale — from pipelines and platforms to private deployments on infrastructure you own. We bridge the gap between local prototypes and enterprise reality.

Book an architecture review → See capabilities

The gap between demo and production.

Most AI initiatives clear the prototype bar and stall at the production line. Models drift, costs spiral, prompts regress silently, compliance becomes an afterthought, and the team that built it spends more time firefighting than shipping.

We close that gap. Whether you're scaling hosted APIs, deploying open-weight models on your own hardware, or running a hybrid of both — we build the infrastructure, pipelines and guardrails that make AI behave like the rest of your production stack.

What we do

Comprehensive operational capabilities for enterprise AI.

From self-hosted foundation models to sophisticated multi-agent orchestration, we provide the operational maturity required to ship AI features safely and sustainably.

01

AIDevOps & MLOps

The operational backbone for AI workloads.
  • CI/CD for models and prompts — automated evals, regression tests, canary deploys, instant rollback
  • Inference platforms — GPU orchestration, autoscaling, multi-model routing, cost telemetry per request
  • Data & RAG pipelines — ingestion, vectorization, indexing and retrieval built for production scale
  • Observability — latency, drift, quality, token economics and hallucination rates in one pane
  • Cost engineering — usage attribution, caching, model right-sizing and FinOps for AI
  • Governance — audit trails, model cards, access controls, EU AI Act & GDPR alignment by design
02

Own Your AI

For workloads where control, cost predictability or data residency matter most.
  • Hardware strategy — sizing, procurement and deployment on-prem, in colo, or on dedicated GPU rentals with honest TCO modeling
  • Open-weight deployment — Llama, Qwen, Mistral, DeepSeek and others, tuned to your latency & throughput targets
  • Private inference gateway — unified API in front of your fleet with auth, rate limiting, routing and audit logging
  • Fine-tuning on your data — your weights, your IP, your perimeter
  • Hybrid architectures — sensitive workloads to private models, scale-out to hosted APIs, all behind one gateway
03

Security & Privacy by Default

Every engagement starts with a threat model.
  • Zero-trust architecture with minimal blast radius
  • Encryption in transit, at rest, and in use where it matters
  • PII and secret detection in prompts and outputs
  • Strict tenant and environment isolation
  • Model and dependency provenance, SBOM for AI systems
  • GDPR, NIS2 and ISO 27001 alignment built in, not bolted on
04

Advanced RAG Infrastructure

Beyond basic vector search — retrieval that finds the right context.
  • Semantic caching for significantly reduced latency and cost
  • Hybrid search (keyword + semantic) with cross-encoder reranking
  • Automated chunking, metadata extraction and embedding pipelines
  • Knowledge-graph integration for complex multi-hop queries
  • Continuous retrieval evaluation to ensure context relevance over time
05

Agentic Systems & Telemetry

When AI takes action, you need to know exactly why and how.
  • Multi-agent orchestration and dynamic workflow routing
  • Execution-trace logging for complex tool-use chains and API calls
  • Autonomous guardrails to prevent destructive or out-of-policy actions
  • Human-in-the-loop approval workflows for high-stakes decisions
  • State management and memory retention across prolonged agent sessions
06

Continuous Evaluation

Stop guessing if the new model is better. Measure it objectively.
  • LLM-as-a-judge pipelines for scaling subjective quality metrics
  • Automated red teaming for prompt injection and jailbreak vulnerabilities
  • Golden dataset curation, management and ongoing regression testing
  • A/B routing and shadow deployments for risk-free model upgrades
  • Statistical validation of output formatting, tone and constraint adherence
Why CTOs work with us

Operational maturity, not another demo.

01

Predictable economics

On hosted APIs or self-hosted infrastructure, you understand cost per request, per workload, per team — before the bill arrives.

02

Operational maturity

AI workloads inherit the same SLOs, observability and incident-response paradigms as the rest of your production platform.

03

Vendor optionality

A unified gateway means switching models, providers or deployment modes is a config change, not a painful multi-month migration.

04

Compliance as code

Audit trails, strict access controls and policy enforcement live in the platform architecture — not a static spreadsheet.

05

Engineering leverage

Your internal team ships product features, not infrastructure. We build the boring, complex foundational layer once, properly.

→

Secure AI gateway

Your models, your data, your perimeter. Complete control over inference workloads, end to end.

How we engage

A structured path to production.

1One week

Architecture review

We assess your current AI stack, workloads and risk exposure, and deliver a written plan with clear cost and architecture trade-offs.

24–12 weeks

Build

We deploy your platform, pipelines, gateway and observability infrastructure — with at least one production workload running end to end.

3Ongoing

Operate

We run it with you, or hand it over completely with the runbooks, dashboards and intensive training your team needs to keep it running.

Get started

Ready to operationalize your AI?

Whether you need a comprehensive architecture review or a fully managed private inference stack, our AIOps engineering team is ready to accelerate your journey to production.

Prefer email? aiops@racetoten.com