---
title: "Generative AI Consulting Company | Practical, Engineer-Led"
description: "Generative AI consulting from engineers: use case selection, build vs buy, RAG and fine-tuning decisions, cost modelling, and an honest read on what your data can support."
image: "https://foundrysoft.co/api/og?type=page&title=Generative+AI+Consulting+Company+%7C+Practical%2C+Engineer-Led&st=Generative+AI+consulting+from+engineers%3A+use+case+selection%2C+build+vs+buy%2C+RAG+and+fine-tuning+decisions%2C+cost+modelling%2C+and+an+honest+rea%E2%80%A6"
url: "https://foundrysoft.co/services/generative-ai-consulting-company"
---

Generative AI consulting · Built in India for US companies

# Generative AI consulting without the maturity model

Our generative AI consulting is engineer-led and short. Which use cases are worth it, whether to build or buy, whether your data supports what you want, and what it will cost at real volume. No maturity model, no transformation roadmap.

Book a 30-min scoping call [See our work](https://foundrysoft.co/work)

No sales script. You talk to the engineers who'd build it.

9+ hrs

Timezone overlap

Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.

100%

You own the IP

Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.

Senior

No juniors hidden on the bill

You work directly with the engineers building your system. No account managers sitting between you and the people writing code.

Weeks

To first deployment

We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.

## What we build

Concrete systems we ship, tuned to your data and your stack.

### Use case shortlisting

A ranked list with baseline numbers attached, so the business case exists before the build does.

### RAG, fine-tuning, or long context

A decision with reasons. Most teams asking about fine-tuning have a retrieval problem, and it is cheaper to find that out first.

### Data feasibility

An honest read on whether your documents, records, and metadata can support what you want, before anyone commits to a date.

### Cost and risk modelling

What it costs at ten times current volume, and what the exposure looks like when a generated output is wrong in front of a customer.

## How we work

01

### Scope & evals

We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.

02

### Build in the open

Weekly demos against real data. You see progress every week and can change direction before it gets expensive.

03

### Ship & instrument

We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.

04

### Hand off or stay

Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.

## Questions, answered

### How long does a consulting engagement run?

+

Two to four weeks typically. Longer than that and we are usually building something, which is a different engagement with different economics.

### Can you help us choose between vendors?

+

Yes. The questions that work are: run it on an input I brought to this call, show me a failed run and your real failure rate, and export a full trace. Those three end a lot of evaluations quickly.

### We have data governance concerns. Where do we start?

+

With what the system can reach rather than what it is meant to do. Intent is not a boundary and permissions are, and that reframing usually resolves the argument in one meeting.

### Will you produce a strategy document?

+

A short one, with numbers and a sequence. What we will not do is a sixty-page transformation roadmap, because those describe a destination without a first step.

## Let's scope your build.

Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.

Start the conversation

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