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AI connected to the systems you already run, tested on your real work.
Remote AI Implementation Services A Dedicated AI Engineer Hourly or Monthly
AI built into the systems you already run, tested on your real work and rolled out to your team by a remote dedicated engineer.
Years of Experience
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AI Implementation From Empiric Infotech LLP
AI implementation is the work between a promising demo and a system your team relies on: connecting the model to your data, securing access, testing it on real cases and getting people to use it. Empiric Infotech LLP gives you a remote dedicated AI engineer to do that work inside your repos and your cloud, with a senior lead reviewing and testing every release. If your rollout also needs someone to lead discovery and work with your stakeholders each day, see our forward deployed engineers.
What AI implementation includes
Six pieces of work that turn a model into something your business runs on.
A check of your data and access
Where the data lives, how clean it is, who may see it and what the model will need. You get a short written view of what can ship now and what needs fixing first.
Integration with your existing systems
APIs and connectors for your CRM, ERP, helpdesk, database and internal tools, with scoped credentials, retries and logs.
Retrieval over your own documents
Search and answers from your files and records, with citations and permissions that match who is asking.
Evals and guardrails
A test set from your real cases, run on each prompt or model change, plus budget caps, approval steps and a clear path to a person.
Rollout and training
A pilot with one team, short training, a usage view, and changes based on what people actually do with it.
Monitoring and cost control
Dashboards for volume, errors and cost per run, and a model swap when a newer one wins on your tests.
How an AI implementation starts
A call, a written scope, then an engineer in your repo.
A scoping call
Thirty minutes on the process you want to change, the systems involved and how you will measure the result.
A written scope
The first workflow, the data it needs, the model options, the tests and who would work on it.
A 7-day risk-free trial on the monthly plan
The engineer gets into your repo and cloud and ships a first slice with logs and a test baseline in the first week.
Hourly or monthly
Hours suit an audit or a single integration. A month suits a rollout with several connected pieces. Seven days' notice to stop.
Two ways to engage an AI implementation engineer
By the hour for a defined piece of work, or monthly for full-time, exclusive capacity across the rollout.
Hourly plan
- A dedicated AI engineer while you have hours booked
- Time tracked, with a weekly report and demo
- Best for a defined scope, with no monthly commitment
- Every release reviewed and tested by a senior lead
Monthly plan
- A dedicated AI engineer, full time, 160 to 172 hours a month
- Best for a rollout with several connected pieces
- Your repo, your cloud and your model keys from day one
- 7-day risk-free trial, monthly billing, seven days' notice to stop
- A senior lead reviews and tests every release
Dedicated team
- Engineers plus a senior team lead who reviews and tests every release
- Add an engineer or designer in 48 hours
- Best for several workflows or departments at once
The first 90 days of an AI implementation
A typical path on a monthly engagement.
- Week 1
Access and a first slice
Repo and cloud access, the first workflow mapped, and the model running end to end on a small real sample with logs.
- Month 1
One workflow in production
Real inputs, real systems, approval steps and a test suite running on each change.
- Month 2
Harder cases and a second use
The edge cases month one surfaced, one more integration, and a wider group of users.
- Month 3 and on
Reliability and cost
A pass on retries and failures, a pass on cost per run, a model swap if one wins, and the next workflow.
A dedicated AI engineer compared with other ways to implement AI
| Empiric Infotech AI engineer | AI consulting firm | Platform vendor's services | In-house hire | |
|---|---|---|---|---|
| What you get | Working software in your systems, from an engineer who stays | A strategy and roadmap, with the build often separate | Setup of that vendor's product | A permanent team member |
| How you pay | Hourly or monthly | Project fee | Part of the licence or a services package | US benchmark: $15,298 a month including employer costs |
| Time to start | 48 hours | Weeks of scoping | After the licence is signed | Months of hiring |
| Model choice | Whichever wins on your tests | Often tied to partners | The vendor's own | Your call |
| Ownership | All code in your repo | Per contract | Your configuration | All yours |
The in-house figure is the BLS median salary of $135,980 (May 2025) plus 35% employer costs. Other cells describe typical arrangements.
Working hours and meeting availability
Our team works 09:30 AM - 07:30 PM IST and a project manager is on call 07:30 AM - 11:00 PM IST, Monday to Friday. Here is exactly when that lands for clients in the US, Europe and Australia, your region first.
US Eastern (New York, Boston, Atlanta) - full team online 11:00 PM - 9:00 AM (next day), project manager 9:00 PM - 12:30 PM (next day).
US Pacific (Los Angeles, San Francisco, Seattle) - full team online 8:00 PM - 6:00 AM (next day), project manager 6:00 PM - 9:30 AM (next day).
UK & Ireland (London, Dublin) - full team online 4:00 AM - 2:00 PM, project manager 2:00 AM - 5:30 PM.
Central Europe (Berlin, Paris, Amsterdam, Madrid) - full team online 5:00 AM - 3:00 PM, project manager 3:00 AM - 6:30 PM.
Australia East (Sydney, Melbourne, Brisbane) - full team online 3:00 PM - 1:00 AM (next day), project manager 1:00 PM - 4:30 AM (next day).
Want it to the half-hour in your own time? Slide through your day and book a slot below.
Why teams use Empiric Infotech for AI implementation
Most of an AI implementation is ordinary engineering: permissions, bad data, retries and integrations nobody documented. A team that has shipped 375+ software projects since 2020 is good at exactly that.
The AI part needs discipline as well. Our engineers write tasks and rubrics used to evaluate frontier AI models, so they test your system on realistic work before your team relies on it.
AI built into real products
BizBoost: AI drafts with human approval
Scheduled posts, AI-written review replies and image captions from Gemini, all routed through an approval dashboard, with queue-based processing that blocks duplicates.
Read case studyEndoDNA: an AI assistant over sensitive data
A HIPAA-compliant platform where clinicians ask an AI assistant about labs, DNA and patient history, with permissions for five user roles.
Read case studySuperintelligence: an AI assistant end to end
A conversational AI assistant with subscriptions and saved history, on OpenAI, Next.js, Node.js, Strapi, Firebase and Flutter.
Read case studyOur Hiring and Engagement Model
Dedicated Resources
Hourly Basis
Hire a Dedicated AI Engineer As per Your Need
As a leading web and mobile app development company in the USA, Empiric Infotech LLP simplified the process of hiring web and mobile app developers. You can hire software engineers in 3 simple steps: Request, Interview, and Hire.
STEP 01
Request
Share your requirements
STEP 02
Interview
Take developer’s interview
STEP 03
Hire
Start risk-free work
Reviews
Frequently Asked Questions (FAQs)
The work of getting AI running inside your business: connecting a model to your data and systems, securing it, testing it on real cases, rolling it out to your team and keeping it working.
Development builds the AI feature or agent. Implementation also connects it to your systems, sets permissions, trains your team and keeps it running. Most projects need both, and one engineer can cover them.
You can pay hourly or monthly, and the rates are in the pricing section on this page. Model and platform usage is billed to your own accounts at cost.
A first workflow usually reaches production in about a month. A first slice runs within the first week.
Yes. That is most of the work: CRMs, ERPs, helpdesks, databases and internal APIs, with scoped access and logs.
With a test set built from your real cases, run on every prompt or model change. Results are reported per case type, and risky actions go through human approval.
Whichever win on your tests at a workable cost: Claude, GPT, Gemini or open source models, on your own accounts.
You do, fully. Code, prompts and tests live in your repo, and an NDA and IP assignment are signed before work starts.
Within 48 hours of sign-off. The monthly plan begins with a 7-day risk-free trial.
GET A QUOTE NOW
Tell us the process, the systems and the result you want, and we will send a written scope with the first workflow.
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