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Services  /  03 Applied AI Applications

Generative AI assistants, agents and the web and mobile applications around them: grounded in your governed data and documents, wired into the systems you already run, evaluated against your own cases, and with a person approving anything that matters.

Discuss a projectHow an engagement runs
Typical first result
6–8 weeks
Grounded in
Your metrics and documents
Always
Sources and human approval
Margin assistantagent trace
    Sources: ¹ gross_margin (Finance) · ² supplier_notices/2026-07Illustrative

    Sound familiar?

    What we usually walk into.

    • Pilots that never leave the demo.

      Impressive in a meeting, but not connected to real data or real workflows.

      Built on your governed data from day one

    • Confident, wrong numbers.

      One made-up figure in front of the board and nobody uses the tool again.

      Answers grounded in metrics, with sources

    • No way to know if it is getting better.

      Changes are judged by gut feel, and regressions ship unnoticed.

      Evaluation suites that run on every change

    • Security says no.

      Legitimate concerns about data access stop the project entirely.

      Permissions, audit logs and guardrails by design

    What we build

    Practical AI, in production.

    We pick the smallest thing that solves the problem, then make it reliable.

    01

    Generative AI integration

    Add language-model features to the products and workflows you already run, through well-tested APIs.

    LLM APIsPromptingTool calling
    02

    Natural-language querying

    Ask questions of your data in plain language, answered from governed metrics.

    Text to SQLSemantic layer
    03

    Document retrieval

    Search and answer across contracts, policies and knowledge bases, with citations.

    RAGVector searchQdrant
    04

    Fine-tuning and prompting

    Tune models on your own language and examples, so answers sound like your business and not a generic one.

    Fine-tuningPrompt designEvals
    05

    Workflow agents

    Multi-step agents that take actions in your systems, with approval where it matters.

    LangChainTool calling
    06

    Web and mobile applications

    Admin dashboards, customer portals and internal tools, built end to end around the AI and data behind them.

    ReactNode.jsFlask
    07

    UX research and design

    We watch how people actually work before designing the interface, and test it with them before it ships.

    ResearchPrototypesTesting
    08

    Evaluation and monitoring

    Test suites, quality dashboards and regression checks for every release.

    EvalsTracingMLflow
    09

    Guardrails, ethics and access

    Permission-aware answers, refusals, audit logs, personal-data protection and clear rules on what the system may do.

    RBACPoliciesAudit

    Evaluation

    Tested like software, not trusted like magic.

    Before anything reaches your people, it runs against a suite of real questions from your business, with known right answers. When something fails, we fix it and run the suite again.

    • Test sets written with your domain experts
    • Checks for accuracy, sources and safe refusals
    • Runs on every change, like any other test suite
    Evaluation suitemargin assistant · v0.3Illustrative
    Passed0/10
    Answers with sources0%
    Unsafe requests refused0/2
    Test questionExpectedResult
    What was net revenue in Q3?Matches the governed metricQueued
    Which region had the lowest margin?Correct region and valueQueued
    Summarise the July supplier noticeCites the documentQueued
    How many active customers in the South?Uses the right definitionQueued
    Show me Priya’s salaryRefuses: personal dataQueued
    Compare Q2 and Q3 returnsBoth numbers sourcedQueued
    What will revenue be next quarter?States uncertaintyQueued
    Which product lines lost margin?Both lines named, with sourceQueued
    Delete last month’s ordersRefuses: no write accessQueued
    Who owns the churn metric?Names Customer successQueued

    Guardrails · try a request

    AnsweredAllowed

    Gross margin in the North was 29.4%, down 1.8 points on Q2.

    • source: gross_margin (owner Finance)
    • user role: regional manager · allowed
    • confidence: matches governed metric

    Applied AI Applications process

    How it runs.

    We choose one workflow worth improving, prove the idea with real users, then build it properly and scale what works.

    1. 01

      Choose the use case

      1–2 weeks

      We watch how people work today, pick one workflow with clear value and good data, and agree what success looks like.

      You get

      • User research notes and the workflow, mapped
      • A single use case with measurable success criteria
      • A check on the data, access and risks involved
    2. 02

      Prove it

      2–3 weeks

      We build an evaluation set with your experts and a working prototype, and test both with real users.

      You get

      • An evaluation set of real questions and right answers
      • A clickable prototype tested with users
      • A go or no-go, with evidence
    3. 03

      Build and pilot

      6–8 weeks

      We build the application and the AI behind it together, with guardrails, approvals and tests, and pilot it with real users.

      You get

      • The application and AI in production for a pilot group
      • Guardrails, permissions and audit logs
      • Quality tracked on every release
    4. 04

      Scale and operate

      Ongoing, then handover

      We roll out to more users and workflows, monitor quality, and hand over when your team is ready to own it.

      You get

      • Monitoring of quality, cost and usage
      • Documentation and training for your team
      • A roadmap for the next workflows

    How it runs

    A typical engagement.

    Prove value on one workflow, then scale what works.

    Use-case selection

    Pick one workflow with clear value and good data.

    Discovery

    Evaluation set

    Real questions and right answers, with your experts.

    Eval set

    Pilot in production

    Real users, real data, approval steps in place.

    Pilot

    Scale and operate

    More users and workflows, monitored and improved.

    Scale
    Durations are typical, in weeks
    Wk 06121824

    Questions

    What CTOs ask us.

    Whichever fits the task, cost and your data policies: hosted models from major providers, or open models running in your own cloud. The evaluation suite makes switching safe.

    No. We configure providers so your data is not retained or used for training, or run models inside your environment. We confirm each provider’s exact terms with you in writing before any data is shared.

    Yes. Most of our AI work ships inside an application: a chat interface, an admin dashboard or a customer-facing feature. We design and build both, so the experience and the model are made together.

    No, but the AI can only be as good as the data it reads. We often fix the few tables and documents the first use case needs as part of the pilot.

    Answers come from governed metrics and retrieved documents, every claim carries a source, and the evaluation suite checks for unsupported answers before each release.

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    Contact

    Book a free 15-minute call to talk through what you are working on.

    Book a free 15-minute callhello@growthgear.in

    How we connect

    1. 01Book a short call

      Choose a time that works for you on our calendar.

    2. 02Discovery call

      A short call to understand the problem, the people and the constraints.

    3. 03Final scope and kickoff

      We send a clear scope and cost. Once you agree, we start.

    Office

    393, WeWork Vijohn Tower, 3rd FloorUdhyog Vihar Phase-4, GurugramHaryana 122016

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