AI gateway and model access
Central, governed access to approved models with routing, quotas and cost attribution per team.
Platforms & Products
Internal AI Platforms
Give every team a secure internal platform for copilots, workflows, and knowledge — without shadow AI sprawl.
- 5×faster AI project delivery on-platform
- 100%of AI usage governed and attributed
- 1paved road replacing shadow AI
Capabilities
One platform, every team enabled
Internal tools and platforms that streamline operations and accelerate your teams.
Shared RAG and tool services
Retrieval, connectors and agent tooling built once, consumed by every use case.
Golden paths and templates
Production-ready patterns that take teams from idea to deployed AI in days.
Governance and cost control
Policies, audit and budgets enforced by the platform — invisible until needed.
How it works
Stop rebuilding the same AI stack in every team
Without a platform, every AI project re-solves auth, retrieval, evals and governance — badly and differently. We build the paved road.
AI gateway
Every model, one governed front door
Teams get self-serve access to approved models through one gateway with authentication, rate limits, content policies and per-team cost attribution — shadow AI ends because the sanctioned path is easier.
- Single API for all approved models and providers
- Per-team quotas, budgets and usage dashboards
- Central policy enforcement: PII, content, audit
Incoming taskcomplex reasoning · 12k ctx
Router
Claude Sonnetquality-critical
GPT-4.1fallback
Llama 70Bhigh volume · low cost
FailoverA/B evalsCachingBudgets
Shared services
Retrieval, tools and evals as platform services
Permission-aware retrieval, a governed tool catalog and eval infrastructure — built once and consumed by every copilot, agent and workflow the company ships after.
- Shared RAG service with source-level permissions
- MCP tool catalog with central auth and scoping
- Eval harness available to every team from day one
SalesforceSlackSAP
JiraSSnowflake
ServiceNowTeamsDrive
OAuthSSOPermissions-awareReal-time sync
Golden paths
From idea to production in days, not quarters
Templates for the common patterns — copilot, RAG app, workflow agent — with security, observability and CI/CD pre-wired. Teams focus on their use case, not the plumbing.
- Scaffolds with guardrails and tracing pre-integrated
- Self-serve deployment inside your existing CI/CD
- Reference apps teams can copy and own
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Use cases
Where internal platforms delivers value
Enterprise AI enablement
Dozens of teams building safely on shared, governed infrastructure.
Shadow AI consolidation
Ungoverned tools and API keys replaced by a better sanctioned path.
Developer AI platforms
Internal tooling that makes every engineering team AI-capable.
Knowledge infrastructure
One retrieval layer serving search, copilots and agents company-wide.
AI cost governance
Spend visibility and budgets across every team and use case.
M&A tech integration
A common AI platform across newly combined organizations.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Before the platform, four teams had four RAG stacks and nobody could answer what we spent on AI. Now a new use case ships in a sprint, and finance gets cost per team in one dashboard.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Kubernetes
- Terraform
- Anthropic Claude
- OpenAI
- AWS Bedrock
- Azure AI
- LiteLLM
- MCP
- Backstage
- ArgoCD
- Datadog
- Okta
FAQ
Common questions
When does a company need an AI platform?
Around the third AI use case. Before that, projects can stand alone; after, duplication, inconsistent governance and unattributed cost compound fast. If teams are already building independently, you're past due.
Won't a platform slow teams down?
Only a bad one. We build for self-service: teams onboard in hours, golden paths are faster than DIY, and governance runs inside the platform instead of as review meetings. The paved road wins because it's genuinely quicker.
Build on a vendor platform or build our own?
Usually a hybrid: cloud AI services where they're commodity, thin owned layers where differentiation and governance live — gateway, retrieval, evals. We help you draw that line deliberately.
How do you drive adoption across teams?
Launch with two or three flagship use cases, publish templates, embed with early teams, and measure onboarding friction. Mandates don't work; a better developer experience does.
Next step
Pave the road for every AI team
We'll assess your current AI sprawl and design the platform that turns it into leverage.