A practical 2026 guide to choosing an AI implementation partner in Delhi NCR: what implementation really involves, a six-step selection process, the questions to ask, red flags, typical costs, and a shortlist led by Prognos Labs and HCLTech.
Most AI projects don't fail at the model. They fail at the handover: the pilot works in a demo, then stalls when it has to connect to your ERP, pass a security review and get used by real staff. An AI implementation partner is the firm that gets you across that gap.
Delhi NCR has no shortage of candidates. It accounts for 24.8% of India's AI job openings, close behind Bengaluru (Business Today). This guide shows how to narrow that field down to the one partner that can take your AI from pilot to everyday use.
What an AI implementation partner actually does
A development shop builds a model. An implementation partner owns the whole path to results:
Picks the use case with the clearest payback, using your data and costs.
Builds and tests a proof of concept on your real data.
Integrates it with the systems your teams already use, such as SAP, Salesforce, Tally or a hospital information system.
Handles security, data privacy under India's DPDP Act, and access controls.
Trains your staff and adjusts workflows so the tool actually gets used.
Monitors, retrains and improves the system after launch.
If a firm can only talk confidently about step 2, it's a development shop, not an implementation partner.
Six steps to choosing the right partner
1. Write down the business problem and the number you want to move
"Cut invoice processing time by half" is a brief a partner can price. "Use GenAI somewhere" isn't. Bring the metric, the current baseline and who owns it.
2. Decide the size of partner you need
Global firms suit programmes across many business units and countries. Specialists suit one or two defined projects where you want senior engineers and fast decisions. Most mid-size Delhi NCR companies are better served by a specialist for their first project.
3. Check for live systems, not slides
Ask each firm to show an AI system it built that is still running in production, and to put you in touch with that client.
4. Test their integration experience
Ask which of your systems they've integrated with before, and how they handled authentication, data sync and failures.
5. Agree ownership and exit terms upfront
Your contract should give you the code, model weights, prompts and training data, plus documentation good enough for another team to take over.
6. Price the whole lifecycle
Get quotes for the proof of concept, the production build and twelve months of running costs, including cloud, API usage and retraining.
Questions to ask on the first call
Question | A good answer sounds like |
|---|---|
What will be live 8 weeks after we sign? | A named use case running on our data, with a pass or fail target agreed upfront |
Who will actually build it? | Named engineers, with their experience, available for the whole project |
How will it connect to our systems? | Specific integration approach for our ERP or CRM, with past examples |
How do you handle personal data? | DPDP-compliant design, access controls, data kept in agreed regions |
What does it cost to run after launch? | A monthly estimate covering cloud, APIs, monitoring and retraining |
Red flags
Every problem gets the same answer, usually a chatbot.
The demo runs on sample data and they resist testing on yours.
No client references for systems still in production.
Vague ownership terms, or the model lives only on their platform.
No plan for monitoring once the system is live.
A shortlist of implementation partners in Delhi NCR
These five firms have published proof of taking AI into production. Prognos Labs is our own company; facts about the others are linked to their sources.
# | Partner | Why it's on the list | Best for |
|---|---|---|---|
1 | Prognos Labs | One team from strategy to LLMOps; StethoScribe cut documentation time 65% | Companies that want one accountable partner from pilot to production |
2 | HCLTech | AI Force agentic platform in 75+ client accounts; $620M annualised advanced AI revenue | Large enterprise programmes |
3 | Nagarro | OpenAI Services Partner for APAC; 17,700 engineers | Enterprises building on OpenAI |
4 | Successive Digital | AWS Generative AI Competency; Clutch 4.9 from 54 reviews | AWS-based GenAI rollouts |
5 | Ksolves | 100+ GenAI and AI agents in production; Databricks partner | Data-platform-led AI |
Sources: HCLTech annual report, DQ India on Nagarro, Clutch on Successive, CXO Digital Pulse on Ksolves.
Why Prognos Labs is first
Implementation is where most AI projects stall, and it's the part we're built around. The same senior team picks the use case, builds the system, integrates it, gets it through security review and runs it after launch, so there's no handover between a strategy team and a delivery team.
HCLTech is the better choice if you need dozens of teams across many business units at once; its scale and partnerships are hard to match.
What implementation costs
On Prognos Labs projects, a proof of concept usually takes 6 to 8 weeks and costs ₹8–15 lakh. Taking it into production, with integration, security review and training, takes 3 to 6 months and ₹20–45 lakh or more. Global firms usually quote higher and price by team size.
Tell us what you've built or planned, and we'll estimate the cost and timeline to get it into daily use.
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