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AI and workflow automation

Your people weren't hired to copy and paste.

We automate the re-typing, chasing and approvals, and get the stuck AI pilot into daily use inside the systems you already run. A person still approves anything that matters.

Talk to Arjun about getting AI live Talk to Rahul about a slow process

People stay in control

AI does the routine work. A person approves what matters.

We automate the re-typing, chasing and approvals, and get the stuck AI pilot into daily use inside the systems you already run. A person still approves anything that matters.

Talk to Rahul about a slow process

Is this you?

Where the working week really goes.

  • Skilled people re-type the same data into four systems.

    Sales, accounts and stock systems, email and spreadsheets, all day long. Then they chase approvals and rebuild a report nobody fully trusts.

  • Your AI pilot impressed the board. Then nothing.

    Great in the demo, shaky on real data, and security still hasn't signed off the launch.

  • Three spreadsheets, three answers for the same number.

    Every review opens with an argument about which file is right, and the decision waits.

Before we automate

Automate a broken process and it just breaks faster.

We watch how the work really runs and cut the steps that shouldn't exist. If every team keeps its own numbers, AI on top just argues faster, so that gets fixed first. Then we automate where it frees the most hours, and a person keeps the final say on anything touching a customer, a payment or a record.

Services

Where the hours come back.

Workflow Automation

Every order gets typed twice, once for sales and once for accounts.

Fix the workflow first, then automate it. We map the work, count the waste, find the bottleneck and cut the steps that shouldn't exist. Then we automate the rest on tools you already own, with a person approving key actions, and measure the results against where you started.

What we do

  • Walk through the workflow with the people who run it, workarounds included
  • Count the waste: waiting, re-typing, rework, chasing and hand-offs that add nothing
  • Redesign the process and remove steps before anything gets automated
  • Automate on tools you already own, with a person approving wherever the work touches a customer, a payment or a record
  • Rank what's left by how much more work gets through for each hour saved at the bottleneck

What you receive

  1. Map of the process today, with the waste counted
  2. Capacity table with the bottleneck marked
  3. Redesigned process with owners
  4. One working automation on your systems, tested on real cases
  5. Runbook, and handover to the process owner
  6. Next automations, ranked by the work they free up at the bottleneck

How it works

The 3-week Workflow Automation Sprint, one workflow at a time.

Measured against your baseline

  • Time from request to done
  • Hours of manual handling each week
  • Errors and rework caught later in the process

Experience behind it, Rahul Nath

  • Rahul Nath's current work covers sales and marketing operations, the path from signed deal to cash collected, CRM improvements, executive dashboards and business process automation.
  • He has built and run operating processes across CRM, revenue controls, deal paperwork and business reporting.
  • He has automated manual reporting and tracking using CRM data and workflow rules.

EliteWorks delivers the full engagement end to end, with the right expertise for each part of the work.

Talk to Rahul about the re-typing and chasing See Workflow Automation Sprint

AI Agents

The demo got applause. Eight months on, it's still a demo.

AI agents that answer from your company's knowledge and do real work inside your tools, drafting, checking and updating records. We test them on real cases, add the safety checks, decide which actions need a person's approval, and launch in stages until your business, security and engineering leads are all happy to sign off.

What we do

  • Agree with the business owner what good looks like, before anything gets built
  • Connect the AI to your documents and systems, and build agents where a task needs them
  • Build tests from real cases, including the ones the pilot got wrong
  • Set each action the AI can take to Inform, Recommend, Act with approval or Act
  • Launch in stages, with monitoring, a runbook and a way to roll back

What you receive

  1. A clear description of the job, with the tests it has to pass
  2. A working system in your environment and your code repositories
  3. Tests and results you can rerun any time
  4. Safety and prompt-injection test results
  5. Monitoring dashboard, runbook and rollback plan
  6. A clear recommendation to go live, give it more time or stop

How it works

Usually the six-week Pilot to Production Sprint, on one clear workflow.

Measured against your baseline

  • Answers or tasks done right on real test cases
  • Time and effort per case
  • Cases handed to a person, and why

Experience behind it, Arjun Murthy

  • Arjun Murthy has built generative and agentic AI, from fine-tuned large language models and AI that answers from company documents to vector search and teams of AI agents working together.
  • He builds AI agents with testing, benchmarking and monitoring in from the start.
  • He has taken AI systems from development to a limited release to full production, with runbooks and lessons from incidents.
  • He has sat on responsible AI reviews for engineering, covering safety controls, audit trails and test benchmarks.

EliteWorks delivers the full engagement end to end, with the right expertise for each part of the work.

Talk to Arjun about your stuck pilot See Pilot to Production Sprint

Run All Your AI From One Place

Five AI tools, three bills, and nobody knows what runs where.

One setup for all your AI, in the cloud or on your own NVIDIA-class servers, with costs and access under control. We decide what runs where, connect the models to your systems and keep watch over them, so nothing runs unseen and no bill takes you by surprise.

What we do

  • List every AI tool and model in use, what it touches and what it costs
  • Decide what runs in the cloud, and what runs on your own GPU servers or edge devices
  • Set up one way to deploy, monitor and control access to every model
  • Show running costs by team and by use

What you receive

  1. List of AI in use, with owners and costs
  2. Plan for running models in the cloud and on your own hardware
  3. A working deployment and monitoring setup
  4. Runbook for your team

How it works

Starts with a short review of what you run today.

Measured against your baseline

  • Monthly AI running cost, by use
  • Time to put a new model into service
  • Models running with no owner or monitoring

Experience behind it, Arjun Murthy

  • Arjun Murthy has deployed AI models on NVIDIA GPUs and on server clusters.
  • He has built systems where several AI agents work together.
Talk to Arjun about your AI setup See Pilot to Production Sprint

Private AI on Your Own Servers

Legal won't let your contracts, drawings or customer data near an outside AI.

AI that runs on your premises, on your data, with nothing sent to an outside model. We pick open models that fit the job, run them on your own servers and connect them to your documents and systems.

What we do

  • Agree which data must stay inside, and which jobs AI should do with it
  • Pick open models that suit the job and your hardware
  • Run them on your own servers, connected to your documents and systems
  • Test the answers on real questions before anyone relies on them

What you receive

  1. Private AI running on your own servers
  2. AI search and answers over your own documents
  3. Test results on real questions
  4. Access rules and a runbook

How it works

Starts with one job, such as answering questions from your own documents.

Measured against your baseline

  • Data sent to outside AI services
  • Time to find an answer in company documents
  • Answers checked as correct on real questions

Experience behind it, Arjun Murthy

  • Arjun Murthy has built AI that answers from company documents, using fine-tuned language models and AI search.
  • He has packaged AI models to run fast on GPUs and deployed them on servers.
Talk to Arjun about AI on your own servers See Pilot to Production Sprint

Reports That Build Themselves

Month-end reports still take a week.

Dashboards that update themselves, with numbers every team trusts. We define each measure once, give it an owner, pull its sources together and add data checks, so any AI you add later starts from one agreed number instead of an argument.

What we do

  • Write one definition, one owner and one source for every key measure
  • Trace each number back to its systems, and fix the places where the versions differ
  • Build dashboards around the decisions people make, not around whatever data happens to exist
  • Set data checks, with an owner for every exception

What you receive

  1. One list of your key measures, each with its owner and definition
  2. One source for each priority measure
  3. Dashboards for operations and for leadership
  4. Data checks and monitoring
  5. Data readiness note for your first AI use case

How it works

A sprint on the numbers your leadership argues about most. Length agreed up front.

Measured against your baseline

  • Time spent matching numbers before each review
  • Measures with one agreed definition and one owner
  • Decisions put off because nobody trusted the data

Experience behind it, Rahul Nath

  • Rahul Nath's current work includes executive dashboards, checks on revenue documents and tracking where people's time goes.
  • He has automated manual reporting and tracking using CRM data and workflow rules.
  • Rahul's approach to one trusted number: every measure defined once, owned once and reported once.

EliteWorks delivers the full engagement end to end, with the right expertise for each part of the work.

Talk to Rahul about your numbers See AI Priorities Review

Pilot to Production

The demo worked. Security won't sign off the launch.

Find out, with proper tests, whether your AI or vision system is ready for real users. We check it against four release checkpoints, build the tests, try hard to break it, and write the launch, rollback and monitoring plan your go-live call rests on.

What we do

  • Check what the system has proven so far against four release checkpoints
  • Build a test suite and repeat tests from real cases
  • Run safety, attack and prompt-injection tests
  • Write a staged launch, rollback and monitoring plan, with a runbook
  • Say plainly what the tests can't show yet

What you receive

  1. Readiness review, checkpoint by checkpoint
  2. Test suite and a fixed set of reference tests
  3. Findings from attack and prompt-injection tests
  4. Launch and rollback plan
  5. Runbook and monitoring dashboard
  6. A clear go-live recommendation

How it works

A 5-day review, or a checkpoint inside the Pilot to Production Sprint.

Measured against your baseline

  • Failures found before your users find them
  • Time from a model change to a tested release
  • Incidents after launch, and time to roll back

Experience behind it, Arjun Murthy

  • Arjun Murthy has taken AI systems from development to a limited release to full production, with runbooks, monitoring dashboards and lessons from incidents.
  • He built reusable test suites, reference-image regression tests and active-learning loops that teams across products adopted.
  • He has led blue-green releases that kept downtime low.

EliteWorks delivers the full engagement end to end, with the right expertise for each part of the work.

Get your AI pilot live See Pilot to Production Sprint

Agent Autonomy Ladder

You decide how much the AI does on its own.

Every action the AI can take sits at one of four levels: Inform, Recommend, Act with approval or Act. Anything that touches a customer, a payment or an accounting record needs a person to approve it.

Pick an example action

Read the approach

  1. Act

    The AI acts on its own within set limits, and every action is logged for review.
  2. Act with approval

    The AI gets the action fully ready. A person approves it before it runs, and the approval is logged.
  3. Recommend

    The AI suggests what to do and why. A person decides.
  4. Inform

    The AI gathers and reports. A person does the rest.

Pilot to Production Sprint

Six weeks from stuck pilot to daily use.

Take one stuck AI pilot out of the demo room and into daily use by real people, in six weeks.

See Pilot to Production Sprint

  1. Week 1: Define success

    What you see
    Workflow agreed with the business owner, with three to five measures of success written down
    What we do
    Today's numbers measured, data access set up, and the security contact briefed
    By the end
    Success measures and starting numbers signed off

  2. Week 2: Build the test set

    What you see
    Pass marks agreed with the business owner
    What we do
    Test set built from real cases, and a risk log started
    By the end
    Test set and pass marks agreed

  3. Week 3: Build

    What you see
    Weekly demo on real data
    What we do
    First working version built on real data
    By the end
    Working version on real data

  4. Week 4: Connect

    What you see
    Demo inside your own systems
    What we do
    Connected to your systems, with limits set on what it may do on its own for each action, and monitoring switched on
    By the end
    Connected and monitored

  5. Week 5: Try to break it

    What you see
    Release review with the business owner, security and engineering
    What we do
    Release tests and deliberate attacks, including prompt injection, where someone hides instructions to trick the AI. Runbook and rollback plan written
    By the end
    Tests passed, or the gaps listed

  6. Week 6: Go live

    What you see
    Release to a small group of named users, then the decision meeting
    What we do
    Live use monitored, and the decision recorded with the results behind it
    By the end
    The business owner's decision, recorded with the results

Who needs to agree

What your COO, CTO, CFO and security team will ask, answered.

For the COO

One workflow, one owner and a starting number agreed early. You see the new process before any software is built, and results are judged on your own numbers.

For the CTO

We work in your systems and code repositories from week one. Models run in the cloud or on your own GPU servers, and you keep the tests.

For the process owner

Your team shows us the real work and checks real cases, instead of sitting through workshops. A person approves wherever the automation touches a customer, a payment or a record.

For the CFO

Judged on hours, turnaround time or errors you already measure. AI running costs are visible from day one.

For risk and information security

Data flows, access and retention are written down before anything is built. Private AI keeps sensitive data on your premises.

Who leads the work

Arjun leads the AI. Rahul leads the process.

Arjun Murthy

Co-founder and CTO

Leads: AI systems, testing and launch

Read Arjun's profile
  • Has taken AI systems from development to a limited release to full production, with runbooks and lessons from incidents.
  • Has built generative and agentic AI, from fine-tuned large language models and AI that answers from company documents to vector search and teams of AI agents working together.
  • Has tuned AI models to run fast on NVIDIA hardware and deployed them at scale.

Rahul Nath

Co-founder and CEO

Leads: Process, owner and results

Read Rahul's profile
  • Has automated manual reporting and tracking using CRM data and workflow rules.
  • Has built and run operating processes across CRM, revenue controls, deal paperwork and business reporting.

Experience from the founders' previous roles and independent consulting.

Where to start

Start with one workflow.

See where to start

  • 3 weeks from startSprintRemote

    Workflow Automation Sprint

    Stop paying smart people to copy and paste. One workflow fixed, automated and measured in three weeks.

    Fixed fee, agreed before work starts

    See Workflow Automation Sprint

  • 6 weeks from startSprintOn site

    Pilot to Production Sprint

    Take one stuck AI pilot out of the demo room and into daily use by real people, in six weeks.

    Fixed fee, agreed before work starts

    See Pilot to Production Sprint

  • First workflow live in 6 to 8 weeks from startSprintOn site

    Multiply Your Best

    Your top performers' way of working, built into AI workflows the whole team uses. First one live in 6 to 8 weeks.

    Fixed fee, agreed before work starts

    See Multiply Your Best

Talk to a founder

Which process does everyone work around?

Or the pilot that never went live. Tell us how the work runs today and which systems it touches. A few lines is plenty.

Talk to a founder

A founder replies personally within one business day, Monday to Friday, India time.

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