ENTERPRISE AI — TRAINING · DEPLOYMENT · OPERATIONS
Futura is the enterprise AI training, deployment, and operations network for complex organizations in China. We build internal AI champions through real work, use model-neutral forward-deployed engineering to move regulated workflows through value, data, and compliance gates into production, and operate them continuously for adoption, quality, cost, and risk.
OUR CONVICTION
Choosing the right work, establishing a business baseline, securing data and system access, handling compliance and exceptions, earning employee adoption, and operating the system as models, workflows, and risks change — that is what is scarce.
Models will continue to multiply, improve, and get cheaper. We therefore do not treat any single model as a moat. We place evaluation, workflows, connectors, permissions, governance, and operations above the model layer: acceptance criteria first, model selection second.
We hold a second conviction. An enterprise does not want permanent vendor dependence; it wants capability of its own. By developing internal champions and shared standards of judgment first, we earn the right to take on the production problems an internal team cannot solve alone. Capability building is not marketing that precedes delivery. It is delivery.
Being precise about position matters more than making it sound large.
WHAT WE DO
These are not three unrelated products. They are the same production path entered at different depths. Most clients start with the capability program, because that is where joint discovery begins.
CAPABILITY BUILDING
A four-week real-work program
Client teams do not learn where the buttons are. They bring their own work: mapping the workflow, establishing KPI baselines, identifying rules and exceptions, becoming internal champions, building safe prototypes, and producing a deploy-or-stop decision package.
Completion is not a certificate and does not promise deployment. The program must let the client separate what an internal team can carry forward, what needs FDE, and what should stop.
PRODUCTION DEPLOYMENT
Fixed-scope production sprint
Only workflows that pass all six hard gates enter FDE. The first two weeks of the statement of work validate production readiness: evals and golden set, data and permissions, interfaces, failure conditions, human escalation, rollback, and acceptance scope. Full engineering begins only after the Production Readiness Review passes.
FDE is not responsible for producing an impressive demo. It is responsible for a system that runs reliably under real load and real constraints, and that a human can take over or roll back.
CONTINUOUS OPERATIONS
Annual continuous operations
Go-live is not the finish line. AI Operations continuously manages adoption, task quality, safety and compliance, model cost, drift, exceptions, incidents, and iteration — deciding together with the client's champions when to optimize, switch models, expand, or roll back.
A system that no longer creates value should be reduced, paused, or retired — not preserved to protect a renewal.
FUTURA PRODUCTION LOOP
The Futura Production Loop turns real enterprise work into governed production systems, and turns single engagements into reusable assets. Each stage must leave artifacts, name an owner, and meet its exit criteria before the next begins.
Select a stage
DISCOVER
Turn "we want to use AI" into a bounded candidate pool with owners and baselines. Interviews cover the business, the people who actually do the work, IT and data, and risk — not only management.
TRAIN
A four-week real-work program builds internal champions and produces the evidence a workflow needs to enter the gate. Teaching serves the project decision; it never displaces validation on real work.
GATE
Six hard gates drive a non-compensatory investment and risk decision. An existing relationship, a founder's rapport, or commercial urgency never substitutes for gate evidence.
DEPLOY
Deliver the gated workflow as a production system with evaluation, permissions, monitoring, human takeover, and rollback. High-risk steps never skip a release stage.
OPERATE
Keep the production system available, controlled, and measurable as the business, the models, and the organization change. "Available" and "actually used, and changing the process" are tracked as different things.
COMPOUND
Convert contractually reusable knowledge into internal assets with a rights provenance, tests, versions, and stated limits of applicability. "We wrote it down" is not compounding.
Stage duration is set by risk and the statement of work. The cadence above is a default reference, not a fixed schedule commitment.
SIX HARD GATES
If one fails, no score on the others compensates — the workflow does not enter production build. A gate has exactly three outcomes: Pass, Revise (time-boxed remediation in an isolated environment), or Stop.
BUSINESS VALUE
Which workflow is being rebuilt, and what measurable value does it create, for whom?
Current baseline, target metric, applicable volume, value hypothesis and how it is calculated. "Efficiency" alone is not an answer.
SIGN-OFFClient Business Owner
OWNERSHIP & BUDGET
Who owns the outcome, who accepts it, who pays, and who operates it after go-live?
Executive sponsor, business owner, champion, budget or procurement path, acceptance authority and escalation route.
SIGN-OFFClient Executive Sponsor
DATA READINESS
Is the data lawfully usable, of sufficient quality, and reliably obtainable under least privilege?
Data inventory, rights provenance, classification, sample quality, retention and deletion requirements, data steward confirmation.
SIGN-OFFClient Data / IT Owner
TECHNICAL FEASIBILITY
Can the systems be integrated, and are quality, latency, cost, and availability achievable?
Target architecture, interface and connector feasibility, candidate model benchmarks, key dependencies, capacity and cost estimates.
SIGN-OFFFutura FDE Lead + Client IT Owner
RISK & COMPLIANCE
Can security, privacy, IP, sector regulation, and the cost of errors be controlled?
Risk classification, human-in-the-loop design, permissions and audit, subprocessor list, threat and misuse testing, rollback strategy.
SIGN-OFFClient Risk Approver
OPERATIONAL READINESS
After go-live, who monitors, handles exceptions, approves changes, and drives adoption?
SLO / SLA boundaries, runbook plan, incident owner, adoption plan, AI Ops responsibility and ongoing budget.
SIGN-OFFClient Process Owner + Futura Mission Lead
Legal rights, data authorization, and critical security requirements are never waived. Acceptable residual risk must be accepted in writing by an authorized client party and recorded in the Gate Decision Record.
ENGINEERING STANCE
Two questions procurement always asks, and vendors usually answer vaguely: how you choose models, and where our data goes.
MODEL NEUTRALITY
Neutrality does not mean never using a vendor's features, and it does not promise zero migration cost. It is the set of engineering practices that keep model choice driven by scenario evidence and keep critical workflows from being locked in by accident.
How it is verified
Comparable candidates, selection rationale, and an exit path exist in the ADR. The eval suite runs repeatably across candidates and produces like-for-like results. Critical workflows are periodically re-evaluated against alternative models and a manual fallback path.
DATA & IP BOUNDARY
What we accumulate is scenario capital, not a customer-data flywheel. Raw data, personal information, business judgment, and trade secrets stay inside the governance boundary the client has approved.
Never leaves the client boundary
Becomes an asset only where the contract allows
How it is enforced
All access uses named identities, least privilege, time limits, and audit. Development, test, and production environments are separated. Where data rights cannot be confirmed, we stop the line, pause processing, and escalate. At contract end, data is returned, deleted, or retained as agreed, with evidence of execution.
WHERE WE START
This kind of work combines value, context, and testability: inputs and rules can be mapped, exceptions need domain judgment, errors carry explicit costs, and outputs must flow back into the systems of record.
INSURANCE & TPA
Claims and underwriting operations dense in rules and human review
ENERGY & INDUSTRY
Contract, ledger, and rule-driven processes in industrial settings
CONTRACT & COMPLIANCE
Clause review, obligation extraction, and approval chains
DOCUMENT OPERATIONS
High-context processes that read, judge, and write back across systems
These are the domains where we build repeatable evidence. They are not a claim that Futura currently covers every enterprise AI use case.
Client names, workflow details, and quantitative results are disclosed only with that client's written authorization. Specific engagement references are available under NDA.
FUTURA AI RESEARCH LAB
The Research Lab connects delivery to productization. It turns recurring production problems into the delivery operating system, evaluation packs, connectors, and named workflow packages. It is not an external foundation-model laboratory, and it is not a separate revenue line.
It measures itself on four auditable outcomes
THE TEAM
Talent density is an input advantage, not the final moat. What is hard to copy is the documented system — selection, coaching, access tiers, QA, and review — proven through production outcomes.
CEO — Capability Programs & Scenario Design
Doctoral researcher, School of Architecture, Tsinghua University; M.S. Advanced Architectural Design, Columbia GSAPP; has taught at Columbia and Syracuse. The teaching background informs how complex knowledge becomes a trainable, feedback-driven organizational system; the systems-design background informs how people, processes, and constraints are abstracted.
Technology & Agent Architecture
Doctoral researcher, College of AI, Tsinghua University; robotics background from Zhejiang University. Leads model selection, agent orchestration, tool calling, and engineering architecture, and owns the implementation of the model-neutrality mechanism.
Forward-Deployed Engineering
Direct-entry doctoral candidate, College of AI, Tsinghua University; dual degree in mathematics and physics and in mechanical engineering. Takes prototypes to reliable, maintainable, reversible production deployments, and enforces evaluation and release discipline.
Product & Compliance
Dual degree in AI and law, Tsinghua University; previously at Manus and ByteDance. Covers product requirements, client communication, and the compliance perspective, and contributes to data-rights and governance templates.
PRINCIPLES
Build the client's judgment and operating capacity first; then take on what their team cannot do alone.
Real data, permissions, exceptions, and organizational constraints define completion.
Define success, failure, and risk boundaries before choosing a model or an architecture.
Reuse must stay inside contractual, data, IP, and confidentiality boundaries.
Baselines, adoption, quality, cost, safety, and expansion — not activity — are the evidence.
Standard method, access tiers, QA, review, and certification are what turn density into scale.
GET IN TOUCH
Tell us about the specific workflow you are considering. A first call usually runs thirty minutes and ends with a clear answer on whether there is a basis to work together — including a recommendation not to start, when the conditions are not there.
