Turn document-heavy operations intoreliable AI-assisted workflows.
AI Systems Studio helps US B2B service and operations teams automate intake, review, routing, and internal knowledge work—with human approval for consequential actions, explicit evaluation criteria, and systems your team can own.
Intake
Approved documents, requests, or records
Assist
Extract, retrieve, classify, or draft
Review
A person approves consequential output
Route
Send the approved result to the right system
The assessment decides which steps need AI, which should remain deterministic, and where a human must stay in control.
Start with a paid assessment
Choose the right workflow before committing to a build.
In 5–10 business days, we map one repeated workflow, define its manual baseline, inspect data and integration constraints, and turn the best-fit opportunity into a fixed-scope pilot with clear acceptance criteria.
Planning price: $1,500–$3,000
One workflow. No production build, legal opinion, or compliance certification included.
What you receive
- Current-state workflow and bottleneck map
- Data, integration, privacy, and failure-risk review
- Ranked AI and automation options—including a do-not-automate decision
- Target architecture and human-review points
- Pilot scope, acceptance criteria, timeline, and fixed-price proposal
Start with the business workflow
Technology is selected after the workflow, source data, review needs, and acceptance criteria are clear.
See what is built, what is illustrative, and what is not yet a case study
Public proof is separated from system patterns so a prototype or mockup cannot be mistaken for a measured client result.
Technical demonstration
Single-document Q&A prototype
A repository-backed prototype demonstrating document extraction, temporary lexical retrieval, and context-constrained generation. It is not presented as a client deployment or production benchmark.
Illustrative implementation pattern
Human-approved document workflow
A synthetic architecture pattern showing intake, extraction, validation, review, and an approved downstream action. No client result or time-saving claim is attached.
Illustrative implementation pattern
Private knowledge assistant
A synthetic architecture pattern for approved sources, retrieval, answer generation, source display, and evaluation. It is not a published client case.
A client case is published only after the work, measurement method, assets, and exact wording are supported and publication permission is documented.
A staged path from uncertainty to operation
Larger commitments follow evidence. A pilot does not become “production” until its data, risks, acceptance criteria, ownership, and operating responsibilities are clear.
Assess
Map one workflow, its baseline, source systems, users, constraints, and failure cases before choosing tools.
Pilot
Prove one bounded use case against agreed acceptance criteria, representative inputs, and review rules.
Implement
Extend a valid pilot with the integrations, controls, documentation, deployment, and training the real workflow requires.
Optimize
Review failures, evaluation results, operating cost, model changes, and bounded improvements after launch.
Founder-led delivery
Work directly with Abhay Rana from assessment through handoff.
AI Systems Studio is an India-based, remote-first practice. Scope, collaboration hours, review cadence, ownership, and support boundaries are agreed before implementation begins.
Clear scope, grounded answers, clean handoff.
One named workflow owner and one measurable baseline before a pilot
Representative inputs and acceptance criteria before model selection
Human review where an incorrect action could matter
Explicit provider, data-flow, retention, and operating boundaries
Failure paths, logs, fallbacks, and handoff included in scope
No performance or business-outcome claim without a measurement record
Questions before you scope an AI system
A good first conversation is specific: the workflow, the data, the users, and what the system should be trusted to do.
What kind of AI system should we build first?
Start with one repeated workflow that has a named owner, representative inputs, measurable volume, and a clear reviewable output. The assessment may also conclude that the workflow should stay manual or use standard automation instead of AI.
Can this work with our private company data?
Potentially, but only after the data flow, provider terms, access rules, retention, deletion, and security responsibilities are agreed. Do not send confidential material through the public contact form.
Do you only build chatbots?
No. Chat is only one interface. A project may use retrieval, structured extraction, classification, drafting, deterministic rules, APIs, or human approval depending on the workflow.
Can you connect AI to tools we already use?
Integration feasibility is checked during assessment. Access, API limitations, data sensitivity, failure handling, and ownership determine what belongs in the first pilot.
Will our team own the system after launch?
Ownership, repositories, hosting, credentials, documentation, and support are defined in the proposal and contract before implementation. They are not assumed from a marketing page.
What should we send before a project call?
Send a non-confidential description of the workflow, approximate volume, current tools, user roles, timing, and what an acceptable output should look like. Representative files can be reviewed later through an agreed secure channel.
Know what to automate before you pay to build it.
Bring one repeated workflow, representative inputs, the current tools, and the outcome you need. The paid assessment turns that into a clear decision and a bounded pilot.