Governed AI infrastructure.
Thinkerly gives product teams a precise layer for routing, observability, prompt operations, and cost control.
1import { Thinkerly } from "thinkerly"2 3const ai = new Thinkerly({4 apiKey: process.env.THINKERLY_API_KEY,5 environment: "production",6})7 8const reply = await ai.chat.create({9 model: "quality-balanced",10 messages: [{ role: "user", content: customerQuestion }],11 policy: "support-assistant",12})1await ai.policies.update("support-assistant", {2 budget: { maxCostPerRequest: 0.04 },3 fallback: ["fast-general", "reasoning-general"],4 trace: true,5})6 7const usage = await ai.analytics.summary({8 range: "last_7_days",9 groupBy: ["model", "environment"],10})Capabilities
The operational layer your AI features need after prototype.
Built around control surfaces: policies, traces, limits, and model decisions you can inspect.
01
Model routing
Send each request to the right model based on latency, cost, availability, and task type.
02
Provider failover
Keep user-facing AI features online when a provider slows down, changes limits, or has an incident.
03
Usage controls
Set budgets, rate limits, and environment policies before experiments become surprise invoices.
04
Observability
Trace prompts, latency, token usage, cost, and errors across teams and environments.
05
Caching
Reuse safe responses and reduce repeated model calls without changing application logic.
06
Prompt operations
Version prompts, compare changes, and promote tested behavior from staging to production.
System
A clean boundary between product and providers.
Keep provider complexity out of application code while preserving visibility and control.
Your product
Keep model calls behind one internal interface.
Thinkerly SDK
Use a small client or the HTTP API directly.
Policy layer
Apply budgets, routing rules, and environments.
Provider network
Connect the models your team approves.
Operations
Trace behavior, costs, errors, and prompt changes.
Use cases
Built for real product workflows.
Start with one high-value AI feature, then expand with the same policies and visibility.
01
Customer support copilots
Route simple requests to low-cost models and escalate complex cases automatically.
02
Internal AI tools
Give teams a governed way to test models without scattering API keys across projects.
03
Production AI features
Ship model-backed workflows with clear logs, cost visibility, and rollback paths.
04
Multi-provider migration
Evaluate new providers behind one interface before committing application code.
Developers
Integrate once. Adjust centrally.
A small API surface for application code, with policies managed outside every feature branch.
1import { Thinkerly } from "thinkerly"2 3const ai = new Thinkerly({4 apiKey: process.env.THINKERLY_API_KEY,5 environment: "production"6})7 8const chat = await ai.chat({9 model: "quality-balanced",10 messages: [11 { role: "user", content: "Summarize this support thread" }12 ],13 policy: "customer-support"14})15 16await ai.analytics.recordOutcome({17 traceId: chat.traceId,18 label: "resolved"19})Commercial
Commercial model.
Early customers are onboarded directly so usage, limits, and support match the workflow.
Preview
For teams evaluating Thinkerly on a focused workflow.
- Managed onboarding
- Core routing
- Usage dashboard
- Email support
Team
For product teams preparing AI features for production.
- Model policies
- Prompt versions
- Tracing and exports
- Budgets and limits
- Priority support
Enterprise
For organizations with governance and deployment requirements.
- SSO / SAML
- Audit controls
- Custom provider rules
- Security review
- Deployment support
FAQ
Questions.
Practical answers for teams evaluating where this belongs in their stack.
Beta access
A sharper control surface for production AI.
The public site stays lean. The product app lives on the app subdomain and beta access is handled directly.