Discover
Which workflows are worth improving with AI?
We turn a broad interest in AI into candidate workflows with a clear scope, owner, baseline, and data requirements.
ENTERPRISE AI — CAPABILITY · PRODUCTION · OPERATIONS
Put AI to work in production, not just in demos.
We work with client teams to choose workflows worth improving, connect prototypes to real systems, and keep tracking results, cost, and risk after launch. The capability and methods stay inside the organization, with a practical path to change models when needed.
- Build capability
- Deploy with confidence
- Improve over time
- 4+2 weekscommon starting rhythm
- 4 weeks building capability first; then about 2 weeks for the readiness review, once prerequisites are in place
- 6readiness questions
- Value, ownership, data, technology, risk, and operations
- 6delivery stages
- From workflow selection to continuous improvement
- Workflow & agent design
- System integration & access control
- Testing, evaluation & exception handling
- Private & isolated deployment
- Model & cost optimization
- Monitoring, audit & rollback
OUR VIEW
A prototype can work. That doesn't mean the business can use it.
Production work has to use real data and systems, handle exceptions and compliance, and earn adoption from the people who will rely on it.
We believe enterprises need AI capability of their own, not permanent dependence on a vendor. We first help client teams develop internal leads and agree on how to assess workflows and accept results. We then work alongside them on production problems their internal teams cannot readily solve alone. Capability building is not preparation for delivery; it is part of the delivery itself.
WHAT WE DO
Three services, from internal capability to ongoing operations
We help clients build internal capability, move worthwhile workflows into production, and keep improving them after launch.
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AI Capability Building
A four-week program built around a real client workflow
We work with the client team to map a real workflow, identify its rules, exceptions, and review points, test a minimal approach in a controlled environment, and develop internal owners.
- A baseline for the current workflow and its problems
- A minimal approach tested on real tasks
- Internal owners and a clear next step
See how it works：AI Capability Building
“4+2” is a common starting format, not a fixed delivery timeline. The first four weeks build capability and test a real workflow. Once data, access, people, and approvals are ready, the production-readiness review usually takes about two more weeks. Further validation, engineering, launch, and operations follow a separate plan agreed with the client.
- Week 1
- Map the workflow, users, systems, and current business performance
- Week 2
- Document rules, exceptions, and human review; build a minimal prototype in a controlled environment
- Week 3
- Define initial tests and acceptance criteria; validate data, access, value, and risk; identify what users need to adopt the workflow
- Week 4
- Confirm internal owners, prioritize use cases, and agree whether to advance, validate further, or pause each one
These outputs help the client decide what its team can continue independently, where specialist engineering is needed, and what needs more validation or should wait.
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Production Engineering
A dedicated engineering engagement for one clearly defined workflow
Once the prerequisites are in place, we connect the approved workflow to real systems and close the gaps against the six readiness conditions.
- A production workflow integrated with existing systems
- Pre-launch testing completed against agreed standards
- Operating guides, acceptance records, and known limitations
See how it works：Production Engineering
Test methods, system-integration components, and workflow templates developed during the project are delivered to the client as the contract provides, reducing repeated work later.
- Engineering
- Workflow automation, system integrations, access controls, logging, and monitoring
- Evaluation
- A reference test set covering normal, edge, exception, refusal, and adversarial cases
- Release
- Steps selected for the workflow's risk and technical conditions, such as offline testing, isolated validation, running alongside the current process, and phased launch
- Handover
- Operating guides, rollback drills, user acceptance testing, and a record of known limitations
Completion is assessed against the agreed criteria for workload, quality, security, human takeover, and rollback.
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Ongoing Operations & Improvement
Ongoing operations after launch
After launch, we track adoption, quality, cost, and changes with the client and support ongoing improvement under the agreed scope.
- Continued tracking of adoption, quality, cost, and risk
- Evaluation, testing, and approval before changes
- Evidence to improve, narrow, pause, or retire the workflow
See how it works：Ongoing Operations & Improvement
Before work begins, both sides agree who approves production changes, who handles day-to-day operations and incidents, and which support hours and response targets are included.
- Value
- Adoption and business outcomes reviewed against the agreed pre-launch baseline
- Quality
- Regular retesting, production sampling, and human review
- Change
- Model, prompt, integration, and rule changes follow a documented review, test, and approval process
- Cost
- Total cost to complete each task successfully, not just the model's per-token price
If a system no longer delivers value, we recommend scaling it back, pausing it, or retiring it.
DELIVERY PROCESS
Six stages from workflow selection to continuous improvement
Every project moves through six stages. At each one, we make clear what needs to happen, who owns it, and what must be true before moving on.
Select a stage
Build capability
Who inside the organization will own and advance this work?
We develop internal AI leads around a real task and use an approved environment to test both the prototype and the team's ability to evaluate it.
Readiness
Which workflows are ready to move toward production?
Together, we confirm value, ownership, data, technology, risk, and operations before deciding whether to begin production engineering.
Deploy
How will it run reliably with real data and real operating constraints?
We move the approved workflow into the production environment and complete every verification agreed before launch.
Operate
How are value, quality, safety, and cost managed over time?
After launch, we track whether the system is reliable, people use it, and the business outcome improves.
Improve & reuse
How can this project make the next iteration faster and safer?
Within the contract and client approval, we organize reusable methods, tests, and components, recording ownership, versions, and permitted uses to reduce repeated work.
Timing depends on scope and risk. The contract sets the project schedule.
PRODUCTION READINESS
Six questions to answer before production engineering begins
Before production engineering begins, both sides settle six things: whether it is worth doing, who owns it, whether the data can be used, whether the systems connect, whether the risk can be controlled, and who runs it after launch. If value is unclear or a major risk remains, we do not proceed.
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H1
Business Value
Is there a real problem and a measurable goal?
See what we confirm：Business Value
Current performance, target metric, expected workload, expected benefit, and an agreed calculation method that turns a broad goal such as "improve efficiency" into a measurable result.
Typical reviewersClient Business Owner
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H2
Ownership & Resources
Who owns it, who accepts it, and are the resources in place?
See what we confirm：Ownership & Resources
Named sponsor, business owner, internal AI lead, budget and procurement plan, acceptance owner, and escalation path.
Typical reviewersClient Project Sponsor
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H3
Data Readiness
Can the data be used legally, safely, and reliably?
See what we confirm：Data Readiness
Data inventory, legal basis and access rights, classification, sample quality, retention and deletion requirements, and data-owner confirmation.
Typical reviewersClient data owner, with privacy, legal, or security reviewers as needed
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H4
Technical Feasibility
Can we connect the systems and meet the quality and cost requirements?
See what we confirm：Technical Feasibility
Target architecture, system interfaces and integration approach, candidate model test results, key dependencies, and capacity and cost estimates.
Typical reviewersFutura Engineering Lead + Client IT Owner
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H5
Risk & Compliance
If something goes wrong, can we detect it, review it, take over, and roll back?
See what we confirm：Risk & Compliance
Risk classification, human review and takeover, access and audit requirements, third-party service providers, and agreed plans, owners, and acceptance criteria for security, misuse, and rollback testing.
Typical reviewersClient risk, security, privacy, or legal owner, plus the Futura Delivery Lead
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H6
Operational Readiness
After launch, who owns outcomes, incidents, and changes?
See what we confirm：Operational Readiness
Service targets, division of responsibilities, operating guide, incident owner, rollout plan, and ongoing budget.
Typical reviewersClient Process Owner + Futura Delivery Lead
We do not launch until data authorization and critical security requirements are met. Manageable risks must have a documented owner, mitigation, and approval path.
TECHNOLOGY & DATA
Technology and data principles
Clients need clear answers to two questions: how we choose models and how we use their data.
Choose the model for the work, not the vendor
We choose models by testing them on real tasks and keep a practical path to change them later. We explain the work, cost, and limits of switching before a decision is made.
- Set test criteria from real tasks before choosing a model
- Compare quality, risk, speed, and total cost
- Retest before switching; public benchmarks are not enough
See model-selection details
Models and prices change. Real-task testing, interface design, and handover planning reduce unnecessary dependence on one provider. Switching still requires retesting and may involve engineering and migration costs.
- We define tests, acceptance criteria, and unacceptable outcomes from real tasks before choosing a model. When more than one option is viable, we compare at least two.
- Candidates are compared on task performance, risk, response time, processing capacity, total cost per successful task, deployment constraints, and the terms for changing or leaving a vendor—not on general leaderboards.
- When the project needs it, we use a client-approved common interface to manage model access, authentication, logs, and versions. Prompts, tool connections, business rules, and test settings are stored separately so they can change without rebuilding the whole system.
- Depending on the project, we can support public APIs, the client's private cloud, on-premises systems, or isolated environments while working with the client's existing cloud and model choices.
- Before changing a model or vendor, we complete the evaluation and regression testing agreed for the project. A higher public benchmark score alone is not a reason to switch. Every choice and its dependency risk is recorded in an architecture decision record.
How clients can verify it
Clients can review the models considered, why one was selected, and how it could be changed later. Where model capabilities and interfaces are comparable, the same core tests can be used for a direct comparison. For critical workflows, we assess backup-model or human-takeover options according to risk, technical feasibility, and project scope.
Use client data only within the approved scope
Client data, internal rules, and trade secrets are handled only within the approved scope. Reusable material must contain no client information.
- Process client data only under the contract and the client's authorization
- Do not reuse client data or identifying material across clients without prior written approval
- Return, delete, or retain project data as the contract requires
See data boundaries
Handled only with client approval and under the contract
- Raw data or data from which a person or client could still be re-identified
- Credentials, permissions, internal URLs, system topology, security weaknesses
- Non-public business rules, pricing, account lists, contracts
- Prompts, logs, reference test sets, and recordings containing client content
- Any use for model training or public benchmarking without separate written authorization
General methods and tools reusable only where the contract allows
- General workflow and interface patterns that contain no client data, internal business rules, trade secrets, or identifying information
- Test methods and test sets built from synthetic data, public data, or data separately approved by the client
- Common failure patterns and repeatable tests that contain no client-specific content
- Integration patterns and governance templates
- Aggregated metrics that cannot be linked to a specific client and are used only with that client's prior written approval
How we enforce it
In environments managed by Futura, access is assigned to named users, limited to what they need and for how long they need it, and logged. Development, test, and production environments are separated according to project risk. Access and responsibilities in client-managed environments are agreed before work begins. If data authorization is unclear, we pause processing. Data held by Futura is returned, deleted, or retained as the contract requires.
WHERE WE START
What makes a good first project
A good first workflow has a clear goal and rules, measurable results, and exceptions that still need expert judgment. Its output must flow back into existing systems, and errors have a real cost, customer, or compliance impact.
Good starting conditions
- You have real data and a business owner and want to move from validation toward production
- The workflow has detailed rules and exceptions, needs human review, and touches multiple systems
- You can establish a baseline for errors, cycle time, volume, or cost
Where we focus
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Insurance claims, underwriting & third-party administration
Rule-heavy workflows with frequent human review
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Energy & industry
Contracts, ledgers, and other rule-based industrial workflows
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Contracts & compliance
Contract review, obligation tracking, and approvals
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Document operations
Work that needs expert judgment and updates across systems
We currently focus on these areas. Fit ultimately depends on the specific workflow, available data, and risk requirements.
We do not disclose client names, workflow details, or quantitative results without the client's written authorization.
THE TEAM
A multidisciplinary team that works directly with clients
Four core team members cover AI engineering, workflow design, production delivery, product, and compliance, and participate directly as each engagement requires.
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Tingrui Zhang
Technology & Agent Architecture
Doctoral student at the College of AI, Tsinghua University. Leads model selection, AI agent design, business-tool integration, and system architecture, with an emphasis on maintainability and preserving a practical path to change models over time.
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Jiajie Zhao
AI Capability Building & Workflow Design
Doctoral student at Tsinghua University's School of Architecture, with an M.S. in Advanced Architectural Design from Columbia GSAPP. Has taught at Columbia and Syracuse. Leads capability building and workflow design, helping clients map complex work, agree on decision and acceptance criteria, and build the internal capability to keep projects moving.
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Yihua Qin
Production Engineering & Delivery
Doctoral student at the College of AI, Tsinghua University, with dual degrees in mathematical and physical sciences and mechanical engineering. Turns prototypes into stable, maintainable production systems with a clear rollback plan, and sets testing and release standards.
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Tongyu Lin
Product & Compliance
Holds dual degrees in AI and law from Tsinghua University and previously worked at Manus and ByteDance. Leads product requirements, client communication, and compliance, and helps define data-use rules and project-management standards.
GET IN TOUCH
Start with a 30-minute conversation about one workflow
Bring one workflow. In 30 minutes, we will assess whether it is worth pursuing, what is still missing, and the most practical next step.