AI & Automation

AI Consulting Services

AI consulting is the discovery phase before you build anything: we review your workflows, data and existing systems, identify where AI can realistically reduce cost or manual work, and hand you a scoped roadmap with effort and ROI estimates — so engineering budget goes toward use cases that will actually pay off.

Most "AI projects" fail before a line of code is written

Teams often start with a tool (a chatbot, an LLM API, an automation platform) instead of a problem. That leads to pilots that never scale, budgets spent on the wrong use case, and integrations that don't match how the business actually operates. AI consulting exists to fix the order of operations: understand the workflow and the data first, then pick the right technique — RAG, an agent, a predictive model, or sometimes no AI at all — and only then build.

Unclear ROI

Leadership is asked to fund "AI" without a clear before/after metric — hours saved, tickets deflected, response time reduced.

Data readiness unknown

Nobody has checked whether the source data (documents, CRM records, tickets) is clean and accessible enough to actually support AI.

Wrong tool picked first

A vendor demo drives the tooling choice before anyone has scoped what the system needs to do end to end.

Our AI consulting process

A typical engagement runs 2-4 weeks depending on how many workflows are in scope, and ends with a written roadmap — not a slide deck of buzzwords.

1

Discovery workshops

We sit with the teams who do the work — support, ops, sales — and map the current process step by step, including the parts that are still manual or spreadsheet-driven.

2

Data & systems audit

We check what data exists, where it lives (CRM, ERP, ticketing, file shares), its quality, and what's realistically accessible via API.

3

Use-case scoring

Each candidate use case is scored on business impact vs. implementation effort, so you can see the shortlist ranked, not just a wishlist.

4

Roadmap & estimate

You get a phased build plan — what to build first, the tech approach (RAG, agent, ML model, automation), and effort/timeline ranges for each phase.

Where AI consulting typically leads

Customer support deflection

Scoping a RAG chatbot against existing help-center content and past tickets to estimate realistic deflection rate before building it.

SaaSEcommerce

Document-heavy operations

Assessing whether contracts, claims, or compliance documents are structured enough for document AI extraction versus needing manual review to stay.

LegalBFSI

Demand & churn forecasting

Checking whether historical sales or usage data has enough volume and consistency to support a reliable ML prediction model.

RetailFMCG

Internal workflow automation

Mapping a manual, multi-system process (e.g. lead routing across CRM and WhatsApp) to identify where an AI agent can safely take action.

Real EstateInsurance

Why scope before you build

Avoid paying full development cost for a use case that a two-week audit would have flagged as low-value.

A written roadmap gives leadership a real business case with effort and impact estimates, not a vague AI pitch.

Data gaps get surfaced early — before an engineering team discovers them halfway through a build.

You get an honest recommendation, including when the answer is "automate this with plain rules, not AI."

Consulting-led builds we've shipped

Consultants who also build the thing

A lot of "AI consulting" is delivered by people who never have to implement their own recommendations. Our roadmaps come from the same team (based in Delhi NCR) that builds RAG systems, AI agents, and ML pipelines day to day, so the plan we hand you is grounded in what's actually feasible to ship — not a generic maturity-model slide deck.

Frequently asked questions

What does an AI consulting engagement actually deliver?
A written document: a prioritized list of use cases scored on impact vs. effort, a data-readiness assessment, and a phased build roadmap with technology recommendations and rough timeline/effort ranges for each phase.
How long does AI consulting take?
Most engagements take 2-4 weeks depending on how many departments and workflows are in scope. Single-use-case reviews can be faster.
Do you only recommend AI, or will you tell us if we don't need it?
We flag it when a use case is better solved with plain automation, a database change, or a process fix rather than AI — that's part of an honest assessment.
Can you consult on an existing AI project that isn't working?
Yes. We regularly review stalled pilots — usually the issue is data quality, an unscoped use case, or a tool chosen before the workflow was mapped.
Does the consulting engagement include the actual build?
It can. Many clients move straight from the roadmap into build with our development team, but the roadmap is also useful if you plan to build in-house or with another vendor.
What industries have you consulted for?
SaaS, ecommerce, real estate, healthcare, BFSI, retail, and manufacturing — the discovery process is industry-agnostic; what changes is the data sources and constraints.

Not sure where AI fits in your business?

Start with a scoped consulting session instead of a guess. We'll tell you what's worth building — and what isn't.

Talk to an AI Expert

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