The data
The pilot ran on a clean sample. Production data turned out to be a mess.
MindAI: The AI Engineering Division of MindInventory
Agents, LLM applications, computer vision, and the unglamorous MLOps work that decides whether any of it still runs in month six.
Trusted by funded startups and growth-stage companies worldwide
INTRODUCTION
MindAI is the artificial intelligence division of MindInventory, a software engineering company that has been building for clients since 2011 from Ahmedabad, India, with offices in the US, UK and Netherlands. Our 70+ AI engineers, data scientists and MLOps specialists work only on AI and data engagements, inside a 300+ person engineering organization. The work covers AI agents and copilots, retrieval and LLM applications, computer vision, document intelligence, and the operations layer underneath all of it. MindInventory has delivered 2,700+ projects for 1,800+ clients across 18+ industries, and holds ISO 42001:2023, ISO 27001:2022, ISO 9001:2015 and SOC 2 Type II, with HIPAA and GDPR delivery experience.
WHY MOST AI PROJECTS NEVER SHIP
Almost nobody comes to us at zero. There’s usually a proof of concept somewhere, built six months ago by an internal team or a consultancy. It demoed well. The business saw the potential. And then it sat there.
We’ve seen the same four reasons often enough to name them.
The evidence
There was no evaluation harness, so when someone changed a prompt, nobody could prove it made things better or worse.
The owner
Nobody owned the model once the consultants left.
The governance
Legal asked where the data goes and the room went quiet.
None of that is an AI problem.
It’s engineering and governance, which is why we start with a readiness assessment instead of a build quote. If the data pipeline isn’t there, a better model won’t rescue the project. It will just fail at a higher cost.
READINESS CHECK
FREE DIAGNOSTIC
IS YOUR BUSINESS ACTUALLY READY FOR AI?
Four questions, about a minute. You'll get a directional read on whether your data, team, and governance can support your AI goals before you commit budget to any vendor, us included.
QUESTION 1 OF 4
How would you describe your business data?
QUESTION 2 OF 4
How prepared is your team for AI?
QUESTION 3 OF 4
Where are you in your AI journey?
QUESTION 4 OF 4
How established are your AI policies?
0
out of 12 - AI Readiness Score
Ready to scale.
Your foundations hold up. The real question is which use case to industrialize first, not whether you can.
Ready in parts.
You can start building, but data cleanup will sit on the critical path. Scope the first project around the data you actually trust.
Foundation work first.
Building now means building on sand. Six to ten weeks on data and governance saves months later.
Too early to build.
You need an assessment, not a vendor. We'd tell you the same on a call, so treat this as a head start.
WHAT MINDAI BUILDS
Eight practices. Most clients start with one and grow into three or four.
01
AI strategy and readiness
Working out where AI actually pays for your business, what it will cost, and whether your data can carry it. Governance sits here too, covering the EU AI Act, model risk, data privacy, and audit trails, helping businesses adopt AI responsibly and confidently.
02
AI agents and digital workforce
Systems that do things, not just answer questions. Single agents, multi-agent orchestration, copilots inside products you already ship, voice agents handling real call volume. In regulated workflows we keep a person in the loop by default. An agent approving a medical claim unsupervised is an incident waiting for a date.
03
Generative AI engineering
Retrieval across your own documents, fine-tuning where retrieval isn’t enough, and the assistant layer on top. Plenty of clients arrive asking for a fine-tune when what they need is better retrieval. We’ll say so before you pay for the wrong one.
04
Machine learning engineering
Forecasting, risk scoring, recommendation, anomaly detection. The everyday models that quietly run a business, built to be accurate enough that someone actually acts on the output.
05
Computer vision and document intelligence
Object detection, visual inspection, video analytics, and turning unstructured documents into structured data. Some of our oldest AI work, and the reason Passio.AI can recognize 2.5 million food items.
06
Data engineering for AI
Modernizing data platforms, unifying what’s scattered across systems, and building the pipelines and governance that decide whether an AI system is accurate or merely confident. Usually the least exciting phase, and the one that determines everything after it.
07
AI integration
Getting AI into the systems you already run: ERP, HRMS, EHR, supply chain, decade-old internal tools. Without breaking them. Most AI projects don’t fail at the model. They fail at the seam where the model meets everything else.
8
AIOps and MLOps
Keeping it working. Evaluation suites, drift monitoring, retraining pipelines, versioning and rollback. Skip this and accuracy slips away without anyone noticing, which is how a pilot can technically succeed and still be worthless twelve months on.
31%
of average ROI is achieved by businesses that invest strategically in AI.
31% of average ROI is achieved by businesses that invest strategically in AI.
Let MindAI help you identify where and how AI can deliver measurable value across your business.
Get Your AI Readiness Report
HOW IS MINDAI DIFFERENT FROM OTHER AI DEVELOPMENT COMPANIES?
Three things, mostly. We won’t start a production build without a proof of concept on your own data. You own everything we build from day one, including model weights and evaluation harnesses. And the AI team sits inside a 300+ person software engineering organization, so the model gets integrated into your actual systems instead of arriving as a notebook.
A team that has only ever built models will hand you something that runs beautifully in a notebook and nowhere near your EHR. 15 years of integration work is what usually decides whether the project ships.
What Has MindAI Built And Put Into Production?
Every project below is a production system with a named client, and the numbers come from after launch, not from a pilot. Most are still running, and several have been with us for years.
Sully AI - clinical copilot and AI workforce
A US healthcare SaaS company wanted to give clinicians a team of AI colleagues, not one more tool to open. We built six agents, covering reception, triage, scribing, clinical consultation, medical coding and care coordination, with an orchestration layer that decides which one handles a given task. It integrates natively with Epic and athenahealth and was built HIPAA-ready throughout.
Outcomes:
2xProviders handling the workload without additional hours
12.5M+minutes of clinical documentation automated
21xreturn on advertising spend
OraQ AI - dental treatment planning AI
Dentists spend the exam explaining risk, and patients accept treatment they can see the reasoning behind. We've worked with OraQ as a dedicated team since 2022, building the risk-scoring models, the digital odontogram and perio charting, and integrations into the practice management systems clinics already run, including Dentrix, Open Dental, ClearDent, Curve and Eaglesoft.
Outcomes:
70%Up to Case acceptance, against an industry norm of 30 to 40%
$250-$330Additional treatment identified per exam
5Major practice management systems
Korial - industrial AI and robotics
Industrial sites run inspection robots from several vendors, each with its own control stack, which leaves operators holding data they can't compare. We built a hardware-agnostic intelligence layer that sits above autonomous robots, drones and fixed sensors, with Unreal Engine 5 digital twin simulation so inspection routes can be tested before anything gets deployed. Clients include Shell, BP and Evonik.
Outcomes:
28%Inspection costs reduced by
40,000+Human inspection hours saved
1M+Autonomous inspections completed
Arrow - healthcare revenue cycle AI
Claim denials are a documentation problem more than a clinical one, and the evidence needed to overturn them sits scattered across systems that don't talk to each other. We built a human-in-the-loop platform for denial investigation, appeal drafting and payer follow-up. It works as a connectivity layer over the EHR and clearinghouse systems the client already ran, replacing none of them.
Outcomes:
85%Claim denials reduced by
18 DaysAccounts receivable days cut from 45
1.5B+Claims processed through the platform
Passio.AI - nutrition recognition
Nutrition apps lose users to manual food logging faster than to anything else in the product. We built visual food recognition that identifies meals from a photo across a 2.5 million item database at 97% accuracy, with portion estimation and barcode scanning for the cases where the camera alone isn't enough.
Outcomes:
97%Recognition accuracy across 2.5M+ food items
1 PhotoPortion size estimated from a single photo
3rd PartyDelivered as an SDK for integration into third-party apps
Read Case StudyCost-effective services from MindInventory made it easier to scale the business efficiently. Their reliability and ability to quickly find the right resources are highly appreciated.
Dmitriy Richard Starson, CEO, Passio.ai
Shoorah - AI mental wellness platform
A UK wellness company wanted always-available support between therapy sessions, in a category where getting the tone wrong carries real consequences. We engineered the AI wellness ecosystem behind the platform, which has since carried over 120,000 AI therapy conversations.
Outcomes:
120,000+AI therapy conversations delivered
42+User retention rate for 30 days
1,000+Five-star reviews across app stores
£7MContributed to a seed round
OUR CERTIFICATIONS AND COMPLIANCE
ISO 42001:2023 is the newest of these and the one worth pausing on. It's the AI-specific standard, and it covers how AI systems are governed across their lifecycle, not just how data is stored. If your procurement team has started asking about AI governance, this is the certification they're looking for, and very few development partners hold it yet.
HOW WE WORK TOGETHER
Engagement Model
Best For
Dedicated AI team
Long-term AI development
Managed Delivery
End-to-end AI projects
AI Consulting
AI strategy and implementation
WHAT CLIENTS SAY
Behind every testimonial is a business problem solved, a system improved, or a product successfully launched. Here is how our clients describe their experience working with MindInventory.
Our business scaled faster with quicker onboarding and installation processes enabled by MindInventory. Their team demonstrated excellent project management skills, and we were particularly impressed with their developers. Communication was smooth and efficient through virtual meetings.
Dimas Lipiz
CEO, RouteMe
Youtube
The project was delivered on schedule, with additional resources provided at no extra cost. MindInventory ensured strong customer success follow-ups and maintained effective communication throughout. Their dedication and client-focused approach truly set them apart.
Prakash Senghani
CEO, Navatech Group Limited
I have had the pleasure of working with MindInventory for more than a year now on our biggest design challenges of creating a full trading app for both web and mobile. From the very start, the collaboration was smooth and effective. The team really understood our vision, and they quickly aligned with our high standards. Together, we designed a platform that feels intuitive, reliable, and engaging for our users. I highly recommend MindInventory to anyone looking for strong design.
Bogdan Ungureanu
Head of UI UX, NAGA.com
A dream was turned into reality with an app that makes it easy for managers and colleagues to share meaningful appreciation at work. The MindInventory team truly listened, understood the vision, and provided flexibility, creativity, and unbeatable project planning. Within months, the app came to life and is now being used and loved.
Erika Migliaccio
CEO & Founder, Upstream HR
Cost-effective services from MindInventory made it easier to scale the business efficiently. The team maintains a timely and communicative process using tools like Jira and Slack. Their reliability and ability to quickly find the right resources are highly appreciated.
Dmitiry Richard Starson
CEO, Passio.ai
A Laravel admin panel and an iOS check-in app were developed with exceptional efficiency, exceeding our expectations. MindInventory consistently met deadlines and completed everything within the allocated hours, ensuring a smooth launch. They are a high-quality and flexible team, with every developer able to meet requirements and communicate effectively.
Rod Ferris
CTO, Pangea Pod Hotel
The Imperial Wealth platform was successfully launched in its beta stage, already receiving overwhelmingly positive feedback from users. The MindInventory team’s energy, effort, care, and persistence played a key role in bringing the platform to life. Their patience and dedication made the journey rewarding, and the progress achieved is something to be truly proud of.
Daniel Ferguson
CTO, Imperial Wealth
Their quick work resulted in an improved Android and iOS product along with an updated admin site. MindInventory made changes and updates nimbly, always adhering to the project’s needs.
Tim Oppenheim
Co-Founder and Partner, In-Kind Pro, LLC
FREQUENTLY ASKED QUESTIONS
Explore answers to common questions about MindAI and our AI engineering capabilities.
MindAI is the artificial intelligence division of MindInventory, a software engineering company founded in 2011 with offices in India, the US, UK and Netherlands. MindAI builds AI systems and applications including AI agents, LLM and retrieval applications, computer vision solutions, and machine learning platforms, then takes them into production with ongoing MLOps support.
No. MindAI is a division, not a separate legal entity. Contracts, certifications and delivery all sit under MindInventory. The name identifies the AI practice and the engineers who specialize in it.
70+ AI engineers, data scientists and MLOps specialists work only on AI and data engagements, inside a 300+ person engineering organization. They are a distinct practice, not application developers reassigned to AI projects.
A scoped proof of concept typically comes in under $25,000 and runs 6 to 10 weeks. Full production AI projects usually land between $25,000 and $150,000. What moves the number most is data readiness, integration complexity and regulatory requirements. Model choice barely registers. Every engagement starts with discovery that produces a fixed estimate before development begins.
Because the delivery model doesn’t carry consultancy overhead. The same engineers who scope your project build it, and there’s no layer of account management between you and them. It isn’t a smaller scope. It’s a smaller bill for the same work.
Structurally, not through prompting. Retrieval grounds answers in your source documents instead of model memory, output schemas constrain what the model is allowed to return, a validation layer checks claims against the retrieved source before an answer reaches a user, and an evaluation harness tests against a fixed benchmark. High-risk actions require human confirmation before execution, never after.
A proof of concept takes 6 to 10 weeks. A production system with real integrations usually reaches first release in 4 to 8 months. Anything touching clinical or financial records takes longer, because compliance review runs alongside development instead of after it.
It comes down to four things: whether your data is centralized and trustworthy, whether your team can operate what gets built, how far your existing AI efforts have got, and whether governance policies exist. Our readiness check scores all four in about a minute. A full assessment goes further and produces a roadmap with prioritized use cases.
We work with them. Most of our AI work is built as a layer over systems that already exist, including EHRs, ERPs, HRMS, claims platforms, clearinghouses and legacy applications. Replacing a working system of record just to add AI is almost always the wrong trade.
Yes, as standard. We sign Business Associate Agreements routinely and have delivered HIPAA-compliant platforms handling protected health information for US clients, including clinical documentation and revenue cycle systems.
You do, all of it. Source code, trained model weights, pipelines and documentation transfer to you on delivery. We don’t retain reusable components or license anything back to you.
Encryption at rest and in transit, role-based access control, isolated development environments, secure APIs, audit logging, and data anonymization where the use case allows. These sit inside our ISO 27001:2022 and SOC 2 Type II controls, so they apply on every project without being reinvented each time.
Through MLOps: evaluation suites scoring outputs against a fixed benchmark, drift monitoring that flags when live data diverges from training data, retraining pipelines, and model versioning with rollback. Without that, accuracy slips away unnoticed, which is how most pilots fail after they’ve technically succeeded.
Healthcare, finance, retail, real estate, education, logistics, manufacturing and sports. Healthcare is the deepest, covering clinical documentation, revenue cycle, risk scoring and patient platforms.
Yes, and it’s how a lot of our AI work runs. Our engineers collaborate with client product, data, DevOps and security teams inside your existing sprint rhythm and tooling, whether we’re augmenting a team you already have or owning delivery end to end.
We tell you, and we don’t proceed to a production build. A PoC that proves an approach won’t work has done its job, at a few weeks of cost instead of nine months of it. That outcome is the reason the PoC exists, and it’s why we don’t start production builds without one.
Yes. Readiness assessments, roadmaps, architecture reviews and governance work are all available standalone. Some clients take the roadmap and build internally.
If you need frontier model research, a foundation model trained from scratch, or an on-premise GPU cluster designed and operated as a managed service, a specialist infrastructure partner will serve you better. We build applied AI systems on top of existing models and infrastructure, and we say so during scoping, not after.
AI INSIGHTS FROM OUR ENGINEERING TEAM
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