AI Sequence · Enterprise AI infrastructure

Trustworthy agentic AI, built in sequence.

We build the infrastructure enterprise agents run on — agent-ready data, verified model access, governed execution — and make every layer accountable.

Explore the platform How we verify

Agents rarely fail for lack of intelligence.

They fail because of what sits underneath them. Three things, in order.

The data isn't ready.

Documents, tables, figures and recordings are written for people. Handing them to a model as flat text loses structure, context and origin — and the agent reasons over fragments.

The model isn't what you paid for.

Upstreams swap models, downgrade tiers or route through consumer accounts. The request never errors. Nothing in the response tells you.

The execution isn't governed.

Capability grows inside individual chats. Nobody can answer who ran what, with which data, at what cost — or trace it afterwards.

AI Sequence builds the three layers underneath the agent — and makes each one verifiable.

See how this composes in a financial advisory agent
The platform

One platform. Three products. One trust layer underneath.

DataSeq prepares the data. TokenSeq delivers verified model access. AgentSeq runs and governs the work. The trust layer — verification, provenance, retention, metering, audit — is not a feature of any one product; it is the floor they all stand on.

Agent-ready data

DataSeq

Turn documents, tables, images and recordings into semantically complete knowledge units — structured, linked, and traceable to the exact source position.

DataSeq in depth → Token-as-a-Service

TokenSeq

One API for text, image and video models. One key, one balance, one bill — and continuous proof that the model you called is the model that ran.

TokenSeq in depth → Agent collaboration & governance

AgentSeq

Put your knowledge, memory and existing agents to work on one platform — used by employees, operated by teams, governed by the enterprise.

AgentSeq in depth →
The trust layer

Five things we can prove, not promise.

01

Model verification

Blind capability tests, upstream provenance checks and tamper probes run continuously. Not tested is never reported as clean.

TokenSeq

02

Data provenance

Every knowledge unit points back to its source: page and block coordinates for documents, timestamps for audio and video.

DataSeq

03

Retention control

Text is never persisted; only billing metadata is kept. Generated media is stored in our own store with a configurable retention period and a verifiable expiry.

TokenSeq

04

Metering integrity

Quote, deduct, then call upstream. One order, one ledger line; prices and multipliers snapshotted into the record; a failed generation is refunded in full.

TokenSeq

05

Governance & audit

Who ran which task, with which capabilities, on whose authority, at what cost — recorded and traceable.

AgentSeq

THE PROBLEM What an upstream can do silently Swap the model behind the name Serve a lower tier or a quantised build Route through a relay you never agreed to Serve from a pool of consumer subscriptions Under-report or over-report token usage HTTP 200 · the response tells you nothing THE CHECKS · RUN CONTINUOUSLY Blind capability tests Same questions, fixed seed, every channel. Hardened until the best model still has headroom. Upstream provenance Official · cloud-hosted · relay · consumer pool — identified by our probes, not by what it declares. Behaviour & usage fingerprints Latency and throughput baselines per model; token-count patterns; refusal and style. THE VERDICT Independent axes of evidence Relay, downgrade and origin are judged separately. Weak evidence cannot launder a strong finding. Three possible outcomes Verified — evidence on every axis Flagged — a finding that is acted on No conclusion — reported as such not tested ≠ tested clean THE ACTION Scored, scheduled, enforced A weighted score across capability, performance, reliability and verification. Untested dimensions are removed, never assumed. onboard evaluate list inspect scheduled · repeats A channel that fails inspection is taken out of service automatically — and can be restored automatically when it recovers. every step recorded · one path for agents and people WHAT A REPORT LOOKS LIKE · illustrative Verification report channel upstream-02 · model frontier-text-01 · 17 checks · run on request ● verified UPSTREAM IDENTIFIED AS Cloud-hosted deployment Consistent with the contracted source. Probes agree on all axes. origin ✓ relay ✓ downgrade ✓ SCORES Channel quality99 Performance88 Model capability93 untested dimensions removed before scoring PER-ITEM mathematics12 / 12✓ code11 / 12✓ instruction following12 / 12✓ tool use10 / 12✓ abstract reasoning11 / 12✓ latency baselinewithin band✓ each item carries its actual cost · wrong = wrong Report content and figures are illustrative. Real reports are generated per channel and per run. key used once · not stored · not logged
Verification report · redrawn · figures illustrative
Read the trust layer
How the sequence composes

A financial advisory agent, built from all three.

A supervising agent with reflection coordinates data agents for structured and unstructured sources and an analytics agent for attribution and exposure. Knowledge and memory come from DataSeq; frontier models arrive through TokenSeq; the whole runs and is governed inside AgentSeq. Coverage: equities across A-share, Hong Kong and US markets; crypto; fixed income.

Client AgentSeq runtime · governance DataSeq knowledge · provenance Sources Investor · advisor · portfolio manager Web · IM · API entry AgentSeq Supervisor agent Plans, delegates, consolidates reflection planning Memory Preferences · holdings · past decisions · conversation personal account session Skills Standardised analytics, reused across tasks performance attribution exposure Sub-agents Structured-data agent Prices, holdings, fundamentals, on-chain and bond market data SQL · time series Unstructured-data agent Filings, research, announcements, meeting records knowledge network · retrieval Analytics agent Stock performance, performance attribution, portfolio exposure calls Skills Governance Who ran which task · under whose authorisation · which capabilities · at what cost organisation · permissions · quotas · audit TokenSeq Frontier models one API verified fidelity metered text · image · video retention control model verification metering DataSeq Knowledge network Business objects, events and topics linked to their source units — every unit traceable to page, block or timestamp market structure indicators instruments corporate events sectors macro strategies regulation Data platform Multimodal parsing → knowledge units → document trees ETL modelling parsing chunking Provenance Documents → page and block coordinates · Audio and video → timestamps. An answer can always be opened where it came from. knowledge units · document trees · source positions Equities A-share · HK · US Fixed income bond markets Crypto market · on-chain Filings annual · interim Research broker · in-house Meeting records calls · transcripts Announcements exchange feeds model calls knowledge retrieval task / data flow
Financial advisory agent · architecture · redrawn
See the solution
Deployment
01

Cloud

Runs on our multi-region edge network and shared upstream pool. Subscription; no infrastructure to operate.

02

Dedicated

Your own environment on our platform, with white-label branding and isolated sessions.

03

Self-hosted

The gateway in your domain. Channels, accounts, data and keys never leave it.

Partners

Build your own AI service on our gateway: white-label sites, multiple domains, per-group pricing, your own billing.

Partners →

Build on a supply chain that can show its work.

Explore the platform