Ship AI features.
Skip the AI infrastructure.

Build custom agents in a dashboard, connect them to your tools and data, and call every one through a single endpoint — with a trace on every request and no lock-in to a model provider.

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14 days free · No credit card · Bring your own model keys

app.sentientone.ai

Ask Support Agent

Ask anything, @ to reference your data

GPT-4o3 toolsKnowledge onTraced

Recent conversations

Where is order #10294?

Resolved · 1.8s · 412 tokens

Summarise this week's churn tickets

Streaming…

Draft a refund policy answer

12 minutes ago

One integration, every major model provider

OpenAI
Anthropic
Google
Meta Llama
Mistral
Groq
Azure OpenAI

1endpoint: for every agent, model and tool
10 minto first reply: signup to traced response
0lines changed: to switch model providers
100%of calls traced: tokens, latency and cost

The honest version

Every team rebuilds the same AI plumbing. We built it once, properly.

Key management, conversation history, prompt assembly, retrieval, token counting, retries, caching, rate limits, cost attribution. Months of work, none of it your product. SentientOne owns that layer, so your app sends a message and gets an answer.

The platform

Six projects you no longer have to run.

Each of these is a quarter of engineering on its own. They arrive wired to each other on the day you sign up.

Agents you configure, not code

Name, system prompt, model, temperature, tools. Change any of it in the dashboard and it is live on the next request — no redeploy, no release train.

One endpoint for all of it

POST /v1/chat/stream. Conversation history, retries, rate limits, streaming and token accounting are already handled behind it.

Never locked to a provider

GPT-4o, Claude, Gemini, Llama, Mistral. The model is a setting on the agent, so a migration is a dropdown instead of a sprint.

Real tools through MCP

Point an agent at an MCP server and it discovers your APIs, databases and internal tools on its own. No hand-written function schemas to maintain.

Answers from your content

Upload PDFs, write FAQs, crawl your docs. Retrieval happens on every call, so agents answer from your material instead of the open internet.

A chat UI you don't have to build

One script tag puts a production chat widget on your site, wired to the agent you just configured and styled to match your brand.

Walk through every feature

No lock-in

Change the model, not your codebase.

The model is a setting on the agent, so the next frontier release is an upgrade instead of a migration. Trial Claude on Tuesday, ship GPT-4o on Friday, keep every prompt and conversation intact.

Why teams move off direct provider APIs

support-agent · model

Claude SonnetAnthropic
GPT-4oOpenAI
Gemini 2.5 ProGoogle
Llama 4Groq

Saved. Zero lines of your code changed.

Connected MCP servers

stripe-api6 tools discovered
postgres-readonly4 tools discovered
zendesk2 tools discovered

Model Context Protocol

Your systems, discovered — not hard-coded.

Expose your APIs and databases through MCP and the agent works out the rest: which tools exist, when to reach for one, and what to do with the result. Add an endpoint upstream and it is available immediately — no schema to update, no release to cut.

See how MCP integration works

Observability

Know exactly what every request did.

Each call becomes an OpenTelemetry trace: the assembled prompt, every retrieval, every tool call, tokens, latency and cost. Debug a bad answer in the dashboard, or export the spans to the observability stack your team already lives in.

Look inside a trace

Trace · req_8f2c

1,412 tokens · $0.0031

agent.run1,840 ms
knowledge.retrieve220 ms
tool.stripe.lookup340 ms
llm.stream1,140 ms

How it works

Live in three steps. No SDK to install.

Create the agent

Name it, write the system prompt, pick a model, attach knowledge and tools. Five fields, no code.

Connect it

Copy an API key, drop in the widget, or register an MCP server. Authenticate once and you're done.

Send a message

One POST. SentientOne handles history, retrieval, tool calls, routing and retries, then streams the reply back.

POST /v1/chat/stream

curl https://api.sentientone.ai/v1/chat/stream \
  -H "Authorization: Bearer $SENTIENTONE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
       "agent": "support-agent",
       "message": "Where is my order #10294?"
     }'

Streams tokens back, calls your tools, logs the trace.

Versus going direct

Same models. Far less to build.

OpenAI, Anthropic and Google ship excellent models. What they do not ship is the platform around them — that part has always been yours to write.

Time to first integrationHours against one endpoint — not weeks of SDK plumbing per provider.
Changing model providerA dropdown in the dashboard. Going direct, it's a refactor.
History, retries, rate limitsBuilt in. Every provider leaves all three to you.
Tool calling across your stackMCP auto-discovery instead of hand-written glue for each API.
ObservabilityOpenTelemetry spans per request, exportable to the backend you run.
Cost controlBring your own keys — pay providers directly, attribute spend per agent.

See the full side-by-side

“Every time you want to add AI to your app you end up writing a ton of code — managing API keys, handling conversation history, engineering prompts, counting tokens. Then six months later a better model comes out and you have to refactor everything. SentientOne just removes all of that.”

Daniel Marsh · CTO

Trust

Safe enough for your real data.

Built for teams whose prompts, customer records and cloud bill all matter.

Your keys, your bill

Attach your own provider keys per agent. Pay OpenAI, Anthropic or Google directly and see exactly which agent spent what.

Self-host when it matters

Run single-tenant inside your own AWS, Azure, GCP or on-prem estate. Prompts, documents and traces never leave your organisation.

An audit trail by default

Every prompt, tool call, token and dollar lands in an OpenTelemetry trace you can export to the stack you already run.

Need a deeper look? Read about self-hosted deployments.

Pricing

Priced like a tool, not a transformation.

Flat subscription, your own model keys, and a 14-day trial on every plan. Cancel whenever it stops earning its place.

Starter

$19/month

For developers putting the first AI feature into a product.

Start free trial

1 agent · 10k requests/mo

1 MCP server per agent
Knowledge — 100 credits †
Embeddable chat widget
Analytics · 30 requests/min
14-day logs & chat history
Community support
Streamable HTTP endpoint
AI Workspace (team chat)

Pro

Enterprise

Custom

For organisations with compliance, scale or data-residency rules.

Talk to us

Unlimited agents & requests

Unlimited MCP servers & knowledge
Self-hosted / single-tenant option
Custom rate limits & log retention
SSO and organisational support
Security review assistance
Dedicated SLA & onboarding

Prices in USD. Every plan includes bring-your-own model keys, the agent chat interface, and a 14-day trial. † 1 credit = 1 FAQ · 5 credits = 1 document.

Questions

Before you sign up.

Still unresolved? Ask us directly — a person answers.

What exactly does SentientOne do?

It is the platform layer between your product and the model providers. You create agents in a dashboard — prompt, model, knowledge, tools — and call all of them through one API. Conversation history, retrieval, retries, rate limits, token accounting, cost attribution and tracing are handled for you, so you can ship an AI feature without an in-house AI team.

How long does the first integration take?

Most teams are streaming a real reply inside ten minutes: create an agent, copy the API key, and POST a message to /v1/chat/stream. There is no SDK to install — any language that can make an HTTP request is already supported.

Can I switch between GPT-4o, Claude and Gemini without changing code?

Yes. The model is a property of the agent, not a dependency in your codebase. Change it in the dashboard and the next request uses the new provider — your integration, prompts and history stay exactly as they are.

What is MCP, and why does it matter here?

Model Context Protocol is an open standard for connecting language models to external tools and data. Expose your REST or gRPC APIs through an MCP server, point an agent at it, and the agent discovers which tools exist and calls them when they are useful — no per-tool glue code to write or keep in sync.

Whose API keys are used, and who pays for tokens?

Yours, on every plan. You attach your own provider keys and pay OpenAI, Anthropic or Google directly at their rates. SentientOne charges only the platform subscription, and shows you token and cost breakdowns per agent so the bill is never a surprise.

Can we run it inside our own infrastructure?

Yes. Enterprise deployments run single-tenant in your own AWS, Azure, GCP or on-prem environment, which keeps prompts, uploaded documents and traces entirely within your organisation's boundary for data-residency and compliance requirements.

What does it cost?

Starter is USD $19/month, Pro is USD $49/month, and Enterprise is custom. Every plan includes a 14-day free trial, bring-your-own model keys, and can be cancelled at any time.

Read first

How this gets built, in detail.

Architecture, the decisions that matter, and the places teams lose months. Free PDFs, no maturity models, one click to unsubscribe.

Free PDF

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Most businesses still run a keyword search bar over a catalogue that customers describe in plain language. The gap between the two is lost revenue — every day. This guide shows how to move from keyword matching to natural-language search, and how to deploy a production-grade Product Search Agent in days, not months.

Free PDF

Improve Your Knowledge Base with AI Agents & RAG

Your team's knowledge is scattered across docs, wikis, and tickets — and answers stay locked away until someone goes digging. This guide shows how Retrieval-Augmented Generation turns that knowledge base into an AI agent that answers in plain language, cites its sources, and stays current, so your team finds what they need in seconds instead of hours.

Free PDF

Improve Your Customer Support with AI Agents

Support queues grow faster than headcount, and every minute a customer waits chips away at trust. Most teams paper over the gap with canned macros and after-hours auto-replies that never actually resolve the issue. This guide shows how AI agents handle the repetitive tickets end to end, escalate the ones that need a human with full context, and give your team back the hours they spend on copy-paste answers.

Browse every white paper

Your first agent is about ten minutes away.

Start on the free trial, wire it into a real screen, and decide with something working in front of you.

Start free trial Talk to the team

14 days free · No credit card · Cancel anytime

Ship AI features.Skip the AI infrastructure.

Every team rebuilds the same AI plumbing. We built it once, properly.

Six projects you no longer have to run.

Agents you configure, not code

One endpoint for all of it

Never locked to a provider

Real tools through MCP

Answers from your content

A chat UI you don't have to build

Change the model, not your codebase.

Your systems, discovered — not hard-coded.

Know exactly what every request did.

Live in three steps. No SDK to install.

Create the agent

Connect it

Send a message

Same models. Far less to build.

Safe enough for your real data.

Your keys, your bill

Self-host when it matters

An audit trail by default

Priced like a tool, not a transformation.

Starter

Pro

Enterprise

Before you sign up.

How this gets built, in detail.

Improve Your Search with Agentic AI

Improve Your Knowledge Base with AI Agents & RAG

Improve Your Customer Support with AI Agents

Your first agent is about ten minutes away.

Ship AI features.
Skip the AI infrastructure.