Architecture before models
We design the control plane, serving layer, data boundaries, and operational model first—so model choice becomes an implementation detail, not a platform bet.
Enterprise AI Infrastructure
Exect
We design, build and optimize enterprise AI platforms.
Private inference, GPU architecture, and production AI operations for organizations that need AI inside their security perimeter.
Reference topology · private AI platformVPC / on-prem / sovereign
Positioning
Infrastructure engineers for enterprise AI—not another model demo.
Deploying an LLM is not the hard part. Designing a platform that is private, sized correctly, integrable with your systems, and operable in production is. That is the work we do.
Private by default
Inference, embeddings, retrieval, and agent runtimes stay inside your VPC, on-prem, or sovereign cloud. Data residency and access control are constraints, not afterthoughts.
Production economics
GPU sizing, batching, caching, and multi-model routing cut cost per request without sacrificing latency SLOs. You get a platform you can operate and forecast.
Systems integration
AI platforms must connect to identity, data warehouses, document systems, CI/CD, and observability. We build for the estate you already run.
Capabilities
End-to-end AI platform engineering.
From reference architecture to production operations—one engineering team accountable for the full stack of private enterprise AI.
- 01
Enterprise AI Architecture
Reference architectures for private inference, RAG, agents, and developer platforms—aligned to security, network, and data governance requirements.
- 02
Private LLM Infrastructure
Deploy and harden open and commercial models inside your perimeter with isolation, auditability, and controlled model promotion.
- 03
GPU Infrastructure Design
Cluster topology, interconnect, storage throughput, and capacity planning sized to real workload profiles—not vendor slides.
- 04
Inference Platforms
NVIDIA Triton, vLLM, and Kubernetes-based serving with autoscaling, canaries, and multi-model orchestration.
- 05
AI Platform Engineering
Internal AI platforms: APIs, SDKs, model catalogs, evaluation pipelines, and self-serve environments for product teams.
- 06
Performance Optimization
Latency and throughput tuning across quantization, speculative decoding, KV cache, routing, and hardware utilization.
- 07
Enterprise RAG
Retrieval systems over governed corpora—chunking strategy, hybrid search, access-aware indexing, and grounded evaluation.
- 08
AI Coding Assistants
Secure coding copilots integrated with source control, policy, and private context—without leaking proprietary code.
- 09
AI Agents
Tool-using agents with bounded permissions, observability, and human-in-the-loop controls suitable for regulated workflows.
- 10
AI Platform Operations
SLOs, incident response, cost dashboards, model drift checks, and runbooks so AI stays reliable after launch.
- 11
AI Security & Governance
Threat models for model APIs, prompt injection surfaces, secrets handling, red-team baselines, and policy enforcement.
- 12
Infrastructure Consulting
Decision support for build-vs-buy, vendor evaluation, capacity roadmaps, and platform ownership models.
Industries
Built for regulated, high-stakes environments.
Medium and large organizations where privacy, auditability, and operational reliability matter more than speed to a demo.
Financial services
Private inference for risk, research, and operations with audit trails and strict data boundaries.
Healthcare & life sciences
On-prem and VPC platforms for clinical and research workloads where PHI and IP cannot leave the estate.
Industrial & manufacturing
Plant-adjacent and cloud hybrid platforms for documentation, quality, and engineering knowledge systems.
Telecommunications
High-throughput inference platforms for customer operations and network intelligence at scale.
Public sector
Sovereign and air-gapped deployments with governance controls designed for procurement and assurance.
Technology & SaaS
Multi-tenant AI platforms and internal developer AI infrastructure for product organizations.
Technology
The stack enterprises actually run.
We engineer on proven serving, orchestration, and security foundations—selected for your workload profile, not our preferred vendor.
Serving
- NVIDIA Triton
- vLLM
- TensorRT-LLM
- OpenAI-compatible gateways
Orchestration
- Kubernetes
- GPU operators
- Helm / GitOps
- Service mesh
Models
- Open-weight LLMs
- Embedding models
- Rerankers
- Specialized small models
Data & RAG
- Vector indexes
- Hybrid search
- Object storage
- Warehouse connectors
Observability
- Metrics & traces
- Token economics
- Eval harnesses
- Incident tooling
Security
- SSO / IAM
- Network isolation
- Secrets management
- Policy engines
Why Exect
Advantage is platform depth—not model access.
Anyone can wire up an endpoint. Few teams can design how models are selected, workloads distributed, GPUs sized, inference optimized, and systems integrated—then keep it reliable in production.
- 01
Multi-model platform design
We route work across large, small, and specialized models instead of forcing every request through a single frontier endpoint.
- 02
Infrastructure that matches demand
GPU fleets sized from measured traffic shapes—batch, interactive, and agent workloads treated as different systems.
- 03
Inference as an engineered surface
Serving stacks are tuned for p95 latency, concurrency, and cost—not demo throughput.
- 04
Enterprise systems fluency
We integrate with the identity, data, and change-management systems already running in your organization.
- 05
Operable after we leave the room
Documentation, ownership maps, SLOs, and handover are part of delivery—platforms must survive without perpetual consultants.
- 06
Security as architecture
Isolation, least privilege, and auditability are designed into the control and data planes from day one.
Delivery
A delivery model built for platforms, not projects.
Clear phases, engineering artifacts at every gate, and ownership transfer designed in from the start.
- 01
Discover & constrain
Map workloads, data classes, latency budgets, compliance boundaries, and existing platform ownership.
- 02
Architecture & sizing
Define reference architecture, model portfolio, GPU topology, and operating model with cost envelopes.
- 03
Build the platform
Stand up serving, retrieval, gateways, CI for models, observability, and secure access paths.
- 04
Optimize & harden
Tune inference, validate SLOs, run security and failure drills, and close operational gaps.
- 05
Transfer & operate
Hand over runbooks, dashboards, and ownership. Optional ongoing platform operations support.
Selected work
Outcomes measured in production terms.
Representative engagements. Client names withheld under NDA. Metrics illustrate the class of result—not marketing composites.
Global bank
Private inference platform for research and operations
Unified serving layer across three model classes with VPC isolation, SSO, and cost controls under a defined GPU budget.
- Latency SLO
- p95 < 800ms
- GPU utilization
- +34%
- Cost / 1M tokens
- −41%
Healthcare network
On-prem RAG for clinical documentation
Access-aware retrieval over governed corpora with audit logging and no PHI egress outside the hospital network.
- Corpus indexed
- 18M docs
- Groundedness
- 92% eval pass
- Time to pilot
- 11 weeks
Industrial OEM
Secure coding assistant across engineering orgs
Private coding assistant integrated with source control and policy, keeping proprietary code off public model endpoints.
- Engineers covered
- 4,200
- Repo leakage risk
- Eliminated
- Adoption
- 67% weekly
FAQ
Direct answers for infrastructure buyers.
If you are evaluating how to own AI infrastructure—not rent a chatbot—start here.
Neither in the traditional sense. We are enterprise AI infrastructure engineers. We design and build the platforms your teams own and operate—architecture, serving, GPU, security, and operations—not slide decks and staff augmentation.
Next step
Request a platform architecture review.
Share your current AI workloads, data residency constraints, and GPU or cloud posture. We respond with a structured assessment of architecture gaps, sizing risks, and a recommended delivery path.
- — No product pitch deck
- — Engineering-led conversation
- — Clear on build vs. buy tradeoffs