Useful generative AI in your team's hands
For CIOs and business leaders who want generative AI working on their own documents and systems - securely, with answers people can check, and with costs known before launch.
Maatara Consulting is an AI-native technology consultancy, with teams in Concord, California and Bengaluru, India, that designs, builds, secures and runs cloud and AI systems. Our generative AI consulting takes you from approved use cases to tools in daily use, with run-costs estimated from day one.
- Discovery workshop
- Architecture and data
- Pilot build
- Evaluate and tune
- Rollout
- Scale
How Our Generative AI Consulting Works
Generative AI consulting should end with people using a tool, not with a strategy deck. We start from the work your teams do every day, ground the model in your own data, and put access, logging and cost limits in place before anyone outside the pilot sees it.
Use cases ranked by value and risk
We list where generative AI could help, then rank each idea by time saved, data sensitivity and how easily a person can check the output.
- A short list you can fund
- Low-risk wins first
Grounded in your data
Answers come from your documents and records through retrieval, with citations so people can verify them. Access follows the user's existing permissions.
- Citations on every answer
- No access to documents a user cannot already see
Private by default
Models run in your cloud tenancy or through enterprise endpoints with logging, so prompts and documents do not train public models.
- Data stays in your tenancy
- Prompts and responses logged for audit
Costs known before launch
We estimate cost per user and per answer during the pilot, set budgets and alerts, and choose smaller models where they are good enough.
- Run-cost estimate before rollout
- Budgets and alerts per tool
Four Generative AI Capabilities We Build
Most organisations start with one of these and add the rest on the same foundation.
Knowledge assistants
Ask-your-documents tools for policies, contracts, product and support knowledge, with citations and permission-aware retrieval.
Document processing
Extraction, classification and summarisation of forms, contracts and reports into structured data your systems can use.
Drafting copilots
Assistants that draft proposals, replies, reports and code in your templates and tone, for a person to review and send.
Secure AI platform
One governed gateway to approved models, with single sign-on, logging, content filters and usage reporting.
Our Generative AI Consulting Services
Pick the pieces you need; everything is designed to run on the same secure foundation.
Use-case discovery workshop
A ranked backlog of generative AI use cases with value, risk and data readiness for each.
Retrieval (RAG) build
Ingestion, chunking, vector search and citation for your document sources.
LLM integration
Generative AI added to your existing apps, portals and workflows through APIs.
Azure OpenAI setup
Private endpoints, networking, keys and quotas configured in your Azure tenancy.
Prompt and template design
Reusable prompts and output formats tuned to your documents and tone.
Model selection and cost tuning
Matching each task to the smallest model that meets the quality bar.
Usage analytics
Dashboards for adoption, satisfaction, cost per answer and top questions.
Team enablement
Training, usage guidelines and champions so tools are used well.
Governance hand-off
Evals, guardrails and policy controls linked to our AI Harness & Governance practice.
Why Teams Choose Maatara for Generative AI Consulting
We are engineers who build and run AI systems, so our advice is shaped by what works in production. Our AI practice is advised by Rudrendu Paul, an applied AI/ML leader with 15+ years of experience and 50+ published papers, and our CEO, Dr. Debasis Bagchi, is the author of 'Application of AI on Healthcare'. We estimate run-costs from day one and hand over tools your own team can operate.
Meet the teamStaff already using public chatbots?
Give them an approved, private alternative that knows your documents.
What Teams Achieve
These are the commitments every generative AI consulting engagement works to.
Run-cost estimate per user and per answer, before rollout
Cited to the source documents it came from
Prompts or documents used to train public models
Adoption, satisfaction and cost reporting after launch
Typical Deliverables
Everything our generative AI consulting team produces stays in your tenancy and repositories.
Use-case backlog
Ranked opportunities with value, risk, data needs and owner.
Reference architecture
How models, retrieval, identity and logging fit your environment.
Working pilot
A tool in the hands of real users, connected to real data.
Evaluation set
Questions, expected answers and scoring to measure quality over time.
Usage and cost dashboard
Adoption, satisfaction and spend per tool and team.
Usage guidelines and training
What staff may and may not do, with short training sessions.
Generative AI pilot that never left the lab?
Find out what it needs to reach real users.
Generative AI in Production
Products we have built where a model works inside a controlled pipeline.
RunQuickSurvey
AI · Build - One survey published to WhatsApp, Telegram, email, web and kiosk, with offline collection, conflict-safe sync and a model that surfaces themes from open-text answers while interviews are still running.
MoreSpace
Build · AI - An index-first pipeline that deduplicates files and moves them between a person's cloud storage accounts, built for around 5 PB of private files with authentication and encryption in transit on every transfer.
SecBoard
Security · AI - A vulnerability command centre that pulls findings from about 25 security tools across eight categories into one store, scores risk by reachability, asset criticality and active exploitation, and generates compliance evidence from the same data.
Generative AI Across How You Go to Market
The best first use of generative AI consulting depends on who your customers are.
B2B & Enterprise
Knowledge assistants for sales, support and legal teams, and drafting copilots for proposals and security questionnaires.
Worried about wrong answers?
See how retrieval and citations make answers checkable.
Our Generative AI Consulting Process
Six steps from a list of ideas to tools in daily use.
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01
Discovery workshop
Interview teams, list use cases and rank them by value, risk and data readiness.
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02
Architecture and data
Choose models and hosting, map data sources and access rules.
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03
Pilot build
Build the first tool against an evaluation set with a small user group.
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04
Evaluate and tune
Measure answer quality, cost and adoption; tune prompts, retrieval and models.
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05
Rollout
Widen access with training, guidelines, dashboards and support.
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06
Scale
Add the next use cases on the same platform and controls.
Our Engagement Model
How a generative AI consulting engagement runs commercially.
Analysis
Free consultation and NDA, then a short call to list teams, tools and data sources.
Design
A proposal with the discovery or pilot scope, milestones and a fixed price.
Development
Pilot built with weekly demos to the user group.
Optimization
Quality, cost and adoption measured and tuned against agreed targets.
Deployment
Rollout to more users with training and an operating runbook.
Support
Monthly managed support, or handover to your IT and data teams.
AI spend rising without clear return?
Get cost per user and per answer before you scale.
Flexible Engagement Models
Three ways to buy generative AI consulting.
Dedicated team
A dedicated team that builds and runs several generative AI tools over time, billed monthly.
Hire a teamFixed scope
A discovery workshop or a first pilot on one use case, at a fixed, milestone-based price.
Get a quoteAdvisory & hourly
Senior advice on model choice, architecture, vendor offers or AI policy, by the hour.
Book advisoryReady to put generative AI to work?
Start with a free generative AI consulting review.
Is Generative AI Consulting a Fit for You?
A good fit if
- Your teams spend hours finding, reading or drafting documents.
- Staff already use public AI tools and you want a safe, approved option.
- You have documents or records the tool can be grounded in.
- You want to know run-costs before you scale.
The wrong start if
- You want a chatbot with no data behind it.
- No one owns the process the tool would support.
- You expect generative AI to make final decisions with no human review.
Generative AI Consulting FAQs
Generative AI consulting at Maatara covers use-case discovery, architecture, data grounding, a working pilot, rollout and measurement. We pick the tasks where generative AI saves real time, connect models to your documents securely, and hand over tools your people use, with quality, adoption and cost tracked from the start.
Still have a question?
Ask an engineer, not a call centre. You get a straight answer within one business day.
Ask our teamGenerative AI consulting at Maatara covers use-case discovery, architecture, data grounding, a working pilot, rollout and measurement. We pick the tasks where generative AI saves real time, connect models to your documents securely, and hand over tools your people use, with quality, adoption and cost tracked from the start.
It can be. We deploy models in your cloud tenancy or through enterprise endpoints that do not train on your data, apply your existing access permissions to retrieval, filter sensitive content, and log prompts and responses. Your security team reviews the design before any real data is connected.
Answers are grounded in your own documents through retrieval and include citations, so people can check them. We measure answer quality against an evaluation set, tune retrieval and prompts, and configure the tool to say it does not know when the sources do not cover a question.
Often both. Packaged copilots are good for general office work. Custom tools win when you need answers from specialist systems, strict workflows, or control over models and cost. We assess your use cases and recommend the mix, including where an off-the-shelf product is the better choice.
A discovery workshop takes days. A first pilot on one use case with real documents and a small user group typically takes a few weeks, depending on data access and security review. The proposal includes a dated plan and the success measures we will judge the pilot by.
It depends on users, questions per day, document volume and model choice. During the pilot we measure cost per answer and per user and estimate monthly spend before rollout. Choosing smaller models for simple tasks and caching common answers often cuts cost substantially.
Ready to Put Generative AI to Work?
Tell us which teams and documents you want to start with. An engineer replies within one business day with a first view of the right use case and architecture. Every generative AI consulting engagement starts with a free consultation.