AI Platform

Choose AI models.
Build agents.
Measure impact.

A unified AI platform for teams that need controlled access to leading LLMs, fast agent prototyping, cost visibility, and business-ready ROI evidence.

AI Platform helps teams access the right models, build useful agents, control cost and quality, and measure which AI workflows are worth scaling.
Model marketplacesynced prices
GPT-5OpenAI
reasoning agents
Claude SonnetAnthropic
writing tools
Gemini 3 ProGoogle
long contextfast
Agent Studioselected model
Sales intelligence agentClaude Sonnet
files tools budget evals
Cost + ROI summarylive
$0cost
$0value
0%ROI
Model access

Model marketplace for enterprise AI teams

Give teams one controlled place to discover, compare, access, and use LLMs from multiple providers.

Unified access

Use one platform instead of managing separate provider accounts, API keys, payment flows, and limits.

Budget and quota control

Set budgets, quotas, and usage limits before teams start scaling consumption.

Model recommendation assistant

Describe your task and get recommended models for cost, quality, speed, or security-sensitive scenarios.

Curated models

synced pricespopular

GPT-5

(OpenAI)

Latency: medium | Context: 128k | $0.05/1k tokens

Best for: reasoning, agents
synced pricespopular

Claude Sonnet

(Anthropic)

Latency: medium | Context: 200k | $0.08/1k tokens

Best for: writing, tools
synced prices

Gemini 3 Pro

(Google)

Latency: fast | Context: 1M | $0.06/1k tokens

Best for: long context
Model selection

Not sure which model to use?

Based on your needs, here are the best options for your use case:

Top recommended models

Claude Sonnet

Anthropic
#1
Expected costmediumLatencymedium
Why this modelStrong writing, tool use, and reliable instruction following for follow-ups and account briefs.Risk / limitationNeeds clear guardrails for claims and customer data.

GPT-5

OpenAI
#2
Expected costmedium-highLatencymedium
Why this modelGood reasoning for account research, prioritization, and structured sales notes.Risk / limitationUse budgets for broad team rollout.

Gemini 3 Pro

Google
#3
Expected costmediumLatencyfast-medium
Why this modelUseful when long context and internal sales documents matter.Risk / limitationValidate output quality on your own examples.
Production path

From model access to production AI agents

Six integrated modules that take you from choosing a model to running a governed, measurable AI workflow in production.

discover

Model Marketplace

Discover, compare, and access models from multiple providers through one controlled workspace.

build

Agent Studio

Create task-specific agents with prompts, tools, files, and workflows.

route

Model Gateway / Proxy

Route requests, manage keys, quotas, budgets, logs, and provider access.

measure

Eval & Observability

Trace agent behavior, compare model quality, monitor latency, and evaluate outputs before rollout.

prove

ROI Evidence

Turn accepted agent work into business evidence: time saved, reviews avoided, tasks completed, and cost reduced.

operate

Command Center

Use a platform assistant to onboard users, recommend models, monitor workspaces, and suggest next actions.

Production pipeline
01Model choice
02Agent build
03Gateway
04Observability
05ROI evidence
06Scaling
Access layer

Built for teams that need reliable AI access in restricted markets

Access to leading AI models is fragmented, unstable, and hard to govern. AI Platform gives teams a single controlled layer for model access, provider routing, budgets, limits, and safe usage.

Multiple providers in one place

Access OpenAI, Anthropic, Google, Azure, and internal models through a single unified API.

Fallback between models and providers

Automatic routing when a provider is degraded or a model hits its quota limit.

Budget and usage transparency

Real-time cost tracking per team, project, and agent. No surprise invoices.

All providers operationalOpenAI · Anthropic · Google · Azure · Internal — all routing normally
Methodology

Show which AI initiatives deserve to scale

Cost, traces, and usage are not enough. Connect agent outputs to accepted business value and build a defensible case for rollout.

Agent action

Customer support responseCode generationMarket analysis

Value event

Time savedIncreased revenueCost reduction

Human approval

Manager validates valueFinance reviewQA sign-off

ROI report

Measurable impactBusiness case validationScaling justification
0×Avg. ROI documented
0+Value events tracked
0Agents in production
0Teams with pilot
Use cases

Start with one model, one agent, one measurable workflow

📈

Sales assistant

Choose a strong reasoning or writing model, build a sales assistant, and track prepared follow-ups and time saved.

recommended modelClaude Sonnet
typical result8× ROI documented
🎧

Support QA agent

Review support conversations, flag risky answers, and measure avoided manual QA hours.

recommended modelGPT-5
typical result12× ROI documented
📄

Finance document agent

Process invoices, contracts, and reports with traceable outputs and accepted value events.

recommended modelClaude Sonnet
typical result15× ROI documented
🔍

Internal knowledge agent

Connect documents and internal knowledge bases, then measure deflected expert requests.

recommended modelGemini 3 Pro
typical result6× ROI documented
💻

Coding assistant

Route coding tasks to suitable models, control budgets, and compare quality across providers.

recommended modelGPT-5
typical result10× ROI documented

Build your own agent

Choose a model, define your workflow, set a budget, and measure ROI in days — not quarters.

Deployment

Move from AI experiments to controlled production

A structured path that takes teams from a first model to a production AI workflow with full governance and measured outcomes.

01Pilot

Start controlled and fast

Pick one use case. Deploy a single agent. Set a budget. Measure the first value events in days, not months.

Model selection and accessAgent Studio setupBudget configurationInitial ROI baseline
02Delivery

Run governed in production

Move your validated agent into production with real governance, trace logging, evaluation, and team oversight.

Gateway and routing rulesEval and observabilityRole-based accessAudit trail
03Scaling

Scale what proves its worth

Use ROI evidence to build the business case for rolling out additional agents and use cases across the organization.

Multi-agent orchestrationCross-team usage reportsROI Evidence reportsCommand Center
Enterprise governance

Control AI usage before it becomes uncontrolled spend

As AI usage grows across teams, governance becomes critical. AI Platform gives you the controls to manage access, spend, compliance, and quality — before they become problems.

Command Center — budget status
Sales team
78% used $500/mo
Finance team
42% used $300/mo
Engineering
91% used $800/mo
Support ops
55% used $200/mo

Access & Keys

Centralized model accessProvider key managementRole-based access control

Spending

Token and cost trackingBudgets and quotas per teamReal-time cost attribution

Observability

Trace and audit logsRequest-level visibilityEval score tracking

Routing

Model fallback and routingLoad balancing across providersInternal model support

Compliance

Sensitive data controlsPII handling policiesRegulatory audit trail
Get started

Access the best AI models, launch agents, and scale what actually pays off.

Start with a measurable workflow, choose the right model, and build the business case before scaling.