AI features in your screens
Drafting, summarizing, classifying and suggesting, inside the workflows your users already know, under your brand.
AI integration · Planning Sprint · 2 weeks · $5,000
AI integration services. For the product you already have.
We add production AI to the software you already run: inside your screens, on your data, through your APIs and permissions, in your own cloud. We rebuild only what the capability needs, and you know the price before the work starts.
No rebuild
We change only what the capability needs
Within a week
The Planning Sprint starts after you book
Takes longer?
No extra cost if within the agreed scope
AI added inside the products of
Work order #2291
Fieldline
- Customer
- Harbor Foods, Dock 4
- Asset
- Walk-in freezer, unit 2
- Visits
- 3 in 30 days
Reading 14 technician notes on this asset
Uses this dispatcher's permissions · runs in your cloud
Illustrative example on a fictional product. Not client work.
What we add
The model is the easy part.
AI integration is adding AI to software that already has customers, data and workflows, so the AI works inside the product rather than beside it. The hard part is the permissions, data, integrations and business rules the AI has to respect, which is where we spend our time. Read the full guide to AI integration in existing software.
Agents that act in your product
Agents that work through your own APIs, inside each user's permissions, with a person approving what matters.
Search and assistants on your data
Answers grounded in your product's records and documents, with citations and per-customer separation.
Document and data extraction
Fields read from contracts, forms and files into your data model, checked before anything is saved.
Recommendations
The next best item, action or match for each user, built on the history your product already holds.
Testing the AI you already have
A feature or agent you already built, tested on your own cases, with what to fix ranked before it reaches every customer.
Try it
Pick a product like yours. Pick a capability.
See where the AI shows up: inside the screen your users already know, not in a separate tool.
1 · Pick a product like yours
2 · Pick a capability
The screen stays the one your users already know. The AI arrives inside it, uses the signed-in user's permissions, and a person decides what to do with it.
Work order #2291
Fieldline
- Customer
- Harbor Foods
- Asset
- Walk-in freezer
- Visits
- 3 in 30 days
Summary of 14 technician notes
The compressor short-cycles after each defrost. The same relay was replaced twice and the fault came back both times.
Add to notesEdit
Uses this user's permissions · runs in your cloud
Illustrative example on a fictional product. Not client work.
Want to click through a real product with AI built in? Try our working demo.
Not a bolt-on
Built into your product. Not beside it.
A chatbot beside your product is quick to show and hard to trust. AI built into the product works on your users' real data, inside their permissions, and is tested before they see it.
AI bolted on
A model beside your product.
- Where it livesA chatbot or widget beside your product
- Whose access it usesOne shared key that can see everything
- What it knowsGeneral answers from a model
- How it is checkedA demo that looked good
- Where it runsSomeone else's platform
- Who answers for itYour team, once the tool is switched on
- How it is pricedHours, until it works
- After releaseYour team finds the problems
Your team still has to make it safe.
Datics AI integration
AI built into the product you already run.
- Where it livesInside the screens and workflows your users already know
- Whose access it usesEach user's own permissions, with data separated per customer
- What it knowsYour product's records and documents, with sources
- How it is checkedTested on your own cases before customers see it
- Where it runsYour code and your own cloud account
- Who answers for itWe own the delivery, to the done criteria agreed up front
- How it is pricedA fixed quote per capability, agreed before work starts
- After releaseMonitoring in place and a two-week warranty
- Inside your product
- Each user's permissions
- Tested on your cases
How it runs
Two weeks to a plan. One capability at a time.
Four steps, from a free call to a capability your customers use. Every one ends with something you keep.
30 minutes
No chargeProduct Review
You show us the product and the idea. We tell you plainly where AI fits and which start makes sense.
You keep
A straight answer on fit
Weeks 1 and 2
$5,000Planning Sprint
Starts within a week of booking. We study your product, data and permissions and define the first capability.
You keep
A ranked plan and a fixed quote
Every two weeks
Fixed quoteBuild Sprints
Built in your code and cloud, tested on your own cases, with a working demo at the end of every sprint.
You keep
A demo every sprint
When live
2-week warrantyIn production
Released to your customers with monitoring in place. The next capability gets its own quote, only if you want it.
You keep
Code, tests and docs in your repositories
Sample Planning Sprint output
End of week 2
Capabilities, ranked
- 1. Work order summariesDepends on: Technician notes APIHigh
- 2. Parts suggestionsDepends on: Parts history exportHigh
- 3. Customer update draftsDepends on: Approval step in the portalMedium
- First capability brief: work order summaries
- Tested on a set of your own past work orders
- One fixed quote to build it
Illustrative example on a fictional product. Not client work.
Want our engineer in your team?
Forward deployed engineers.
Our engineer joins your standups, repositories and reviews, and stays until the outcome is live. It starts with a one-week, $2,500 Outcome Sprint.
See forward deployed engineersAlready built a demo?
Start with the AI Scorecard.
It tests your feature on 50 of your own cases and ranks what to fix before it reaches every customer. $5,000, two weeks, and the fee counts toward production.
Take a demo to productionTry it
Not sure which start fits?
Four questions, and we show the start that fits your product, with what it costs.
- 01Is your product live, with customers using it?
- 02Have you already built an AI feature or agent?
- 03Do you know the outcome you need?
- 04Do you have engineers who would work alongside ours?
Four questions
Find the right way to start.
Answer all four and we show the start that fits your product, with what it costs. Your answers go with your booking, so the call starts where you are.
In practice
How it ran in products that already had customers.
Six live products, AI added inside each one, and how the work ran.
Real estate and hospitality
CleanHaus
An AI in the property chat that acts for each owner, manager and cleaner, only within that person's role.
How it ran
- Built in CleanHaus's own repository, AWS and Cognito logins
- Demo recordings shared with its CTO as each feature landed
- Every agent run traced and logged, with a runbook for failures
Sales training (EdTech)
Insurance Sales Lab
An AI prospect its agents can call any time, scored on the company's own script and checklist.
How it ran
- Inside its platform, with no separate sign in
- Admins own the scripts, scenarios and scoring standard
- A demo recording after each step, its feedback in the next sprint
Healthcare
Datascape
Reads compensation terms from any physician contract into its data, inside its own HIPAA cloud.
How it ran
- Onboarded into its Slack, GitHub and AWS under a signed BAA
- Every change shipped as a versioned release it could test
- Each release left code, docs and a walkthrough in its account
Document management
NenoBrief
Cited answers inside its live document system, from models running on its own GPU host.
How it ran
- Runs on its own AWS GPU instances, beside its existing system
- Each user's access checked before any passage is ranked
- Every finding from its test rounds fixed and rechecked with it
Healthcare practices
Peerlogic
Turned raw phone events into trusted call records for its practice AI, inside its own product.
How it ran
- Scope set with Peerlogic's CTO before any build
- Every change through its pull requests and CTO review
- Built in Peerlogic's own Google Cloud environment
Financial services
A regulated investment platform
AI suggests each deal field from the issuer's files, and the issuer applies, edits or rejects it.
How it ran
- Each agent delivered as an API for its own .NET team
- Integrated through the client's own branch
- Demoed, then into the client's acceptance testing
More products we added AI to
- Transcripts U.S.Title page details read in the agency's browser and published only after the agency approves each field
- Coast TechnologyDealer site shoppers ask in plain words and get real units from the dealer's own inventory
- OCCIIts Microsoft Copilot agent tested in its own tenant on live job cost data, every answer checked against SQL
- A regulated investment platformAI recommends deals and drafts interest for investors, and every investment decision stays with people
- A European team AI chat providerIts own features carried onto a current open source base, open to more AI models, in its own cloud
- AmoveA Slack assistant that answers from its wiki pages, FAQs and pricing tables, with sources
What clients say
AI their customers already use.
The founders and CTOs whose products we added AI to, in their own words.
CleanHaus · App and AI agent live
“CleanHaus has been moving quickly to deploy our app and Broomy AI Agent with the key assistance of the Datics team. We couldn't have reached this point without their excellent bench of engineers.”
Datascape · Live, hired again
“Umair is a proven professional who knows what it takes to get things done. His engineers have a deep well of knowledge they can tap for tackling complex development problems. I'm looking forward to continuing work together.”
CleanHaus · Every feature walked through before release
“I want to recognize and let you know that your work is having a truly positive outcome. Our recent tests are going so much better!”
HireInfluence · AI sentiment analysis platform
“Umair and his team at Datics have done an outstanding job developing SenTrac, our AI-powered sentiment analysis platform. What I have appreciated most is their willingness to understand our vision, work through complex requirements, and incorporate feedback as we refine the platform.”
5.0 average client rating
Security and your stack
Your customers' data stays theirs.
We work in your repositories and your AWS, Azure or Google Cloud account, and choose the model per capability by testing it on your own cases. More on AI security in existing products.
Your accounts, your access
We work only in your repositories and cloud, with access you grant and can revoke at any time.
Each user's own permissions
The AI sees only what the signed-in user can see, and data stays separated per customer.
Tested before release
Every capability is tested on your own cases, including for data leaks and misuse, before customers use it.
Your agreements
We work under your NDA, or a business associate agreement where health data is involved, as we do for Datascape.
We work with
- AWS Bedrock
- Azure AI
- Google Vertex AI
- LangChain
- LangGraph
- CopilotKit
- Databricks
Moving clouds as well? See cloud and AI modernization.
Questions
AI integration services, answered.
See what the Planning Sprint delivers, or how we work from the first call to production.
Do we need to rebuild our product to add AI?
No. We add AI to the product you already run, through your existing APIs, data and permissions, and change only what each capability needs. Where something must change first, such as a missing API or a data issue, the Planning Sprint names it and it is part of the fixed quote.
How long does it take to add AI to an existing product?
The Planning Sprint takes two weeks and ends with a plan and a fixed quote for the first capability. That capability is then built in two-week sprints; how many depends on your product and the capability, and you know the price before work starts.
How soon can you start on a new AI capability?
The Planning Sprint starts within a week of booking.
What if building the AI capability takes longer than planned?
Within the agreed scope, that is on us: the fixed quote does not change. If you add to the scope, we agree the change and its price with you before any work on it starts.
What happens after the AI capability is released?
Every capability comes with a two-week warranty after release: anything that does not meet the agreed done criteria is fixed at no extra cost. Further work is quoted on its own, only if you want it.
Who is accountable for delivering the AI capability?
We are. Datics owns the delivery of each capability, with our own team and process, to the done criteria agreed in the Planning Sprint and at the fixed quote. You review the work and the demos; you do not manage the people doing it.
Who builds the AI capability?
Senior Datics engineers who have shipped AI in production inside live products, backed by the wider Datics team on architecture and applied AI. If you would rather have our engineer work inside your own team, that is a forward deployed engagement.
What do AI integration services include?
AI features in your existing screens, agents that act in your product, search and assistants on your data, document and data extraction, recommendations, and testing of AI you already built. Each starts with a Planning Sprint and is built in two-week sprints at a fixed quote, in your code and cloud.
What do AI integration services cost?
You can start with the $5,000, two-week Planning Sprint, or with the $5,000 AI Scorecard if you already built an AI feature. Production work is quoted at a fixed price per capability, and the sprint fee counts toward it.
Which AI model should we use?
It depends on the capability, its cost per task and where your data must stay. We work with AWS Bedrock, Azure AI and Google Vertex AI and the models they offer, and choose per capability by testing on your own cases rather than by brand.
Who owns the code?
You do. The work lives in your repositories and cloud accounts, and everything built for the capability is yours.
How do you keep customer data safe?
The AI uses each user's own permissions, data stays separated per customer, and it can run inside your cloud account when your contracts require it. Every capability is tested for data leaks and misuse before it reaches customers, and we work under your NDA, or a business associate agreement where health data is involved.
Have a capability in mind for your product?
Show us the product and the idea. In 30 minutes you will know where AI fits, what it depends on and which start makes sense.