Generative & Agentic AI Consulting, Built on a Proven Roadmap
Most leaders know AI matters, but can't see where it fits or what it will return. NeenOpal removes that uncertainty with a structured AI assessment, then builds the AI agents, conversational analytics, and document intelligence solutions it uncovers.
AI platforms we build on
AI assessment
An AI Assessment Built Around Your Business
The hardest part of AI is knowing where to begin. Our AI assessment turns that uncertainty into a plan built entirely around your business. A prioritised list of the use cases worth pursuing, what each will cost to run, and the return you can expect.
- 01
Requirement Gathering
Three to four hours with your team is all it takes to understand your business, your data, and exactly where you want AI to take you.
- 02
AI Assessment
Within two to three weeks, we deliver an evidence-based assessment of exactly where AI fits across your business and which opportunities are worth acting on first.
- 03
Growth Roadmap
You get a 12-month complete roadmap naming the best use cases for your business, and the ROI each can deliver.
The AI journey
One Connected AI Journey, From Use Case to Production
Where AI Pays Off
Two to three weeks to map where AI fits in your business, what it will cost, and what it will return; no obligation to build with us.
AI Assessment ServicesSee It Work
See real generative AI agents and prototypes at work in our Demo Hub, on scenarios close to your own, before you commit to building anything.
Explore Our AI SolutionsProduction Ready Agents
We build the AI agents, conversational analytics, and document intelligence your assessment surfaces, harnessing RAG, orchestration, and guardrails, plus a framework to switch AI providers freely.
AI Agent DevelopmentSafe To Deploy
Every agent's tool access is scoped with least privilege through our DevOps framework, with guardrails tuned to whether it's internal, chatbot-facing, or customer-facing. Our approach to AI governance is backed by ISO/IEC 42001 certification for AI management.
Our AI governance approach
Outcomes
Outcomes Enterprise Leaders Measure Us By
- Financial Impact Delivered to Clients
- $750M+
- Reached North Star outcomes within 90 days
- 85%
- Faster Go-to-Market With AI Acceleration
- 8X
- Cloud cost savings across operations
- 60%
What we build
What We Build
NeenOpal offers three generative & agentic AI consulting services for enterprise scenarios where AI creates the most value.
AI Agent Development
Custom AI agents built through agent harnessing: large language models, RAG, multi-agent orchestration, knowledge graphs, vector databases, and guardrails, delivered through hands-on AI agent consulting.
Learn moreConversational Analytics
Talk to your data and get charts and insights back, plus analytics on your conversations to uncover the why behind the numbers.
Document Intelligence
Turn documents and unstructured inputs into decision-ready information, from RFQ automation (turnaround cut from two days to two hours) to KYC-style checks where a person only reviews and decides.
CIOs, IT directors and CDOs who know AI matters but need a generative AI development partner with a credible plan.
AI in production
Leading the Agentic AI Conversation, With Real Systems to Show
NeenOpal's AI leads walk through agents already live in client environments: an RFQ assistant cutting quotes from two days to two hours, and a conversational analytics agent answering business questions in plain language. Real agents. Real clients. This is NeenOpal leading the AI conversation, backed by delivery.
AI readiness
Is Your Data and Infrastructure AI-Ready?
It comes down to what you're already running, and it breaks down into three common cases.
APIs Available
If APIs exist, we integrate with them directly, no extra engineering needed.
No APIs
If they don't, our AI can operate your existing forms and workflows directly, so integration is rarely required.
The One Hard Case
The one real exception is a desktop-only app with no browser access. Outside of that, readiness is rarely the blocker.
Our core lens is simple
How We Think About AI ROI
The case for AI is the hours it gives your team back, and we can show you that math on your own numbers before you commit to anything.
- 01
Where the Effort Really Goes
80 to 90% of most processes is collecting and preparing information. Only 10 to 20% is acting on it.
- 02
What AI Takes Off Your Plate
Generative AI automates that, leaving your people to do the part that needs judgment, like reviewing and deciding.
- 03
What Your Team Gets Back
Even after a small review-step overhead, you're operating at a fraction of the original effort. The same team does far more.
Model switching
What About When You Need to Switch Models?
Model providers move fast. Here's how we keep you from getting stuck with one.
The Problem
Teams that build early on one model provider, often an early OpenAI GPT deployment, get stuck: the same prompt behaves differently on a newer or cheaper model, whether that's a different OpenAI model, Amazon Bedrock, or Azure OpenAI.
Our Framework
We give you a migration pipeline that lets you switch models whenever you want, as often as every couple of months. We pair every migration with a model evaluation step, so you can see how the new model performs against your own prompts before you switch.
The Result
A year-long migration effort becomes a repeatable process you own, with no re-hiring specialists each time. No starting over with a fresh prompt library each time a better or cheaper model appears.
AI solutions
Scalable AI Solutions, Already Live and Delivering Results
Every example below is running today, inside a real client's systems: a chatbot booking actual flights, agents turning quotes around in hours, wireframes generated from a plain-language brief. This is NeenOpal's shipped work.
AI-Powered Shop Floor Assistant: From Machine Fault to Cited Fix
Supervisors describe the fault in plain language and get an answer cited to your own SOPs. When the procedure itself is wrong, the assistant proves it and proposes the correction.
100%Answers cited to your own SOPs and manuals
Read more
InsightIQ: From Business Question to Board-Ready Answer
Your dashboards show what moved, not why. InsightIQ is a conversational AI data analyst that lets executives ask questions of your database in plain English, and shows the SQL behind every number.
100%Of answer steps show their SQL
Read more
AI Analyst Agent: From Business Question to Sourced Answer in Seconds
Ask your business data a question in plain English and get a trusted, sourced answer in seconds. Deploy an AI analyst agent in a 5-day sprint.
4 DaysTo first AI-powered insight
Read more
Engagement models
Choose How You Want to Scale Generative AI with Us
Managed Team
A dedicated NeenOpal team handles your generative AI project end-to-end, from the assessment through to deployment.
Schedule a strategy sessionStaff Augmentation
Add NeenOpal's AI and data-certified experts to your team, scaling capability quickly when your project demands it.
Find available expertsArchitecture Review
We start with a review, then prove it with a POC, or move straight to full deployment, your call.
Request a project estimate
Case studies
Related Case Studies
Revolutionizing BI Dashboard Creation with Mokkup.ai & AWS Bedrock-Powered Gen AI
Manual dashboard wireframing had become a hard bottleneck on decision-making, keeping dashboard creation out of reach for the business users who needed it most. Mokkup.ai set out to make it instant and accessible with a system that could generate and refine dashboards from simple prompts. Working closely with AWS, NeenOpal helped bring that vision to life, leveraging Claude models through AWS Bedrock, zero-shot AI prompting, and proprietary layout engines to automatically generate, review, and optimize dashboard designs. The platform now serves over 250,000 active users across 60+ countries.
Read case study
Modernizing Automotive Damage Assessment with AI and AWS
Our client, a leading automotive repair service provider, faced increasing challenges in quickly and accurately assessing vehicle damages for insurance claims, repairs, and customer estimates. Traditional manual inspections consumed time, increased errors, and delayed decision-making. By using Amazon Bedrock and OpenSearch vector search, the client transformed their damage processing workflow, enabling faster, scalable, and more accurate repair cost assessments, resulting in enhanced customer satisfaction and operational efficiency.
Read case study
LLM Migration from OpenAI to Amazon Bedrock
A leading marketing advisory specializing in wealth management and investor communications was running a core generative AI capability for AI powered, FINRA and SEC compliant email generation on OpenAI GPT models. While the solution delivered early value, growing concerns around data security, compliance governance, escalating API costs, and single vendor dependency created an urgent need to migrate to a more enterprise grade and sovereign AI stack.
Read case study
Client reviews
What clients say about working with us
Each member of the team is interested and knowledgeable about the process!
NeenOpal Intelligent Solutions is understanding and invests heavily in the project at hand. The engagement resulted in an optimized inventory and increased sales, meeting the expectations of our internal team!
Kapila PereraIT DirectorSinger Sri Lanka PLCVerified review on ClutchThey are attentive to detail and have shown us they can solve difficult problems.
We’re excited to partner with NeenOpal for their collaborative approach, on-time delivery, clear communication, and attention to detail. Their ability to solve complex problems, paired with creative design and forward thinking, sets them apart.
Joyce Ann McGinnFounder & COOCompris, Inc.Verified review on ClutchI like that the team is incredibly hard-working, talented, and good with people.
NeenOpal is a hard-working, talented team that consistently brings fresh ideas. Their domain experience reshaped our MIS reporting and delivered deeper insights, helping us approach complex business challenges with greater clarity and improved resource productivity.
Saransh ChaudharyCEOVenus Remedies LimitedVerified review on ClutchNeenOpal delivers high-accuracy analytics that drive reliable, KPI-driven outcomes.
It’s been a pleasure working with NeenOpal from the outset. They delivered our data lake on time and consistently demonstrate high numerical accuracy and strong analytical expertise. By meeting key KPIs, they support smooth operations and measurable business outcomes.
Saurabh BholaCTOThe Healthy Mummy Pvt. Ltd.Verified review on ClutchFind out where AI pays off in your business
Start with an AI assessment: a few hours of your time, then a 12-month roadmap with the use cases worth pursuing and the return each can deliver.
FAQ
Generative AI Consulting FAQs
NeenOpal is a generative AI consulting company helping enterprises turn AI ambition into a measurable plan.
How do we start with generative AI consulting services?
With an AI assessment: two to three weeks and a few hours of your time, ending in a 12-month roadmap with the solutions that fit, expected recurring AI cost, and projected ROI. No obligation to build with us.
How do we know if our systems are AI-ready?
If your system is operational and web-based, it's almost certainly ready. We integrate with your APIs where they exist; where they don't, our AI can operate your existing forms directly, so integration is rarely a blocker.
Can we switch AI models later without a huge rebuild?
Yes. Our model-migration framework leaves you with a pipeline to move between models whenever you want, turning a year-long effort into a repeatable process.
What's the ROI case for generative AI?
In most processes, 80 to 90% of the work is collecting information and only 10 to 20% is acting on it. AI automates the former, so your team does far more with a light review step. That saved effort is the ROI.
What's the difference between an AI agent and a chatbot?
A chatbot answers questions. Once it has access to tools, meaning it can take real actions like checking a system, filing a ticket, or looking up a record, it becomes an agent.
How do you handle governance and guardrails for agents in production?
It depends on the workflow. Internal, employee-facing agents get guardrails that keep anything off-policy from going out. Customer-facing agents, like the booking assistant we built for an airline-ticketing platform, get tighter limits on exactly what the agent can and can't do. Every agent's tool access is also scoped through our DevOps framework, so a program only ever reaches the systems it's meant to.