Built for software already in use
Most of our work is inside software people already rely on, with its integrations, permissions and business rules, changing only what the AI needs.
AI integration for established software
Add production AI to the product that already works.
Established software, whether you sell it or run on it, comes with years of workflows, data, integrations and permissions. We build AI into it, in your cloud and under your name, and ship it to production without derailing your roadmap.
Start with a Build Sprint or a Planning Sprint. Already built AI? Start with the AI Scorecard.
learn.northstarcampus.com / practice / results
Northstar Workforce Academy · Practice results
AI scoredLearner · scenarioScoreFlag
D. Hughes · Price objection3/6Skipped needs check
R. Silva · Price objection2/6No next step
T. Nguyen · Price objection3/6Rushed close
Northstar Coach
3 learners below target
- 1 skipped needs check
- 1 no next step
Coaching notes drafted from each call
To D. Hughes: Ask about current coverage before presenting a plan.
Review feedbackAssign practice
Illustrative example, not client work.
Your assistant, your name, built inside your product.
AI shipped inside existing products
Best fit
Built for software that already has something to protect.
If two or more of these sound like your product, we are a good fit.
Users you cannot disrupt
People rely on it every day, so AI has to arrive without breaking how they work.
A roadmap that is already full
Your engineers know the product, but their next quarters are already committed.
A prototype that never shipped
You tried AI, and it stalled before it reached real users.
AI is now a business priority
Customers are asking for it, and competitors are already showing it.
Industries
Eight industries we know from the inside.
We have shipped AI into live products in each of them. Every industry page has working demos you can try.
- EdTech7+ case studiesAdvising and intake · Skills practice · Course drafting
- Automotive11+ case studiesWebsite sales assistant · Lead follow-up · Inventory insight
- Healthcare10+ case studiesPatient intake · Clinical notes · Call follow-up
- Fintech5+ case studiesDeal drafting · Due diligence · Cited research
- Property Management5+ case studiesTurnover scheduling · Crew updates · Contractor outreach
- MarTech6+ case studiesCampaign sentiment · Creator matching · Account scoring
- Legal Tech5+ case studiesDeposition summaries · Transcript indexing · Redaction
- GovTech9+ case studiesPayment help · Records answers · Batch balancing
Building something outside these eight? We have also shipped AI into enterprise, data and other B2B software. Book a Product Review or see all case studies.
The problems we solve
Calling a model is easy. Making it work inside your product is the job.
After 60+ AI projects, we see AI stall in the same four places. Here is each one, what we do about it, and where to start.
AI keeps losing to the roadmap.
Where it stalls
AI is a priority, but every initiative competes with an already committed roadmap.
What we do
We pick the first capability worth shipping, and our team builds it, so your roadmap keeps moving.
Start with the Planning SprintThe product was not built for AI to use.
Where it stalls
Your workflows, data, APIs and permissions were built for people and code, not agents.
What we do
Expose the context, tools and boundaries AI needs, and leave the rest of the product as it is.
Start with the Build SprintAI can answer, but it cannot do the work.
Where it stalls
The AI gives answers, but users still click through the workflow themselves.
What we do
Connect AI to your workflows, APIs, permissions and approvals, so it finishes the task itself.
See AI agent developmentThe demo cannot survive production.
Where it stalls
Tenancy, security, audit, evaluation and deployment turn a working demo into a production problem.
What we do
Build tenancy, permissions, audit and evaluation in from day one, inside your own cloud.
Start with the AI Scorecard
What AI-native looks like
AI that does the work inside the workflow.
Useful AI needs the same context, tools, permissions and approval rules the product already relies on.
Before8 clicksWith AI1 approval
The product as it ships today: every decision reached by filtering, reading and clicking.
Example: an inspections tool · Illustrative, not client work
app.meridiancompliance.com / inspections / queue
MeridianDashboardInspectionsSitesFindingsR. Harper
Inspection queue
4 open · Northeast
SearchRegionStatusInspectorDue date
RefSiteInspectorDueStatus
INS-8841Harbor Point TerminalR. AlvarezOverdue 6dEscalate
INS-8846Bridgeport Depot 4K. ItoOverdue 3dEscalate
INS-8852Lakeview ProcessingM. BooneOverdue 1dReview
INS-8860Cedar Falls PlantD. NkemeluIn 2dScheduled
Manual filtering, manual selection, manual escalation. Eight clicks per decision.
How we work
Ways to start, each at a fixed price.
Pick the one closest to where you are. Every start fee counts toward production.
Which is closest to you?
Want your product inside Claude and ChatGPT? See the connector. Not sure? Answer four questions.
Build Sprint
$10,000· 3 weeks
One buyer outcome, from your own use case or one of our 41 ready-made AI modules, delivered as two or three related capabilities in your product's staging in three weeks.
What you keep
- A head start: a working module adapted to your product, or your own use case built from the ground up
- The capabilities working in your staging, behind a feature flag
- Test results on your own cases, against the bar agreed in week one
- The code in your repositories, and the test set to run on every release
- A fixed quote to take them to production; the $10,000 counts toward it
If the outcome does not meet the bar we agreed in week one, we keep working at our cost until it does, for up to two more weeks.
Every start begins with a free 30-minute Product Review.
After the start: production in waves, each at a fixed quote with a two-week warranty. AI Care, $1,500 a month per capability, if you want it.
Proof
Production AI, live in software people use every day.
Four of the products we have built AI into. Each was live, with real users and rules we could not break.
RV shoppers get units from live stock, with towing fit computed, not guessed
Dealer sites offered static forms and filters. Now shoppers ask in plain words and get real units from the hourly feed, with the reason each one fits.
- Runs
- In production, in Coast's own AWS account
- Grounded in
- Each dealer's inventory feed, website pages and documents
Insurance agents practice calls with an AI prospect, scored on the company's script
Practice used to wait for a human coach, and feedback varied. Now agents call an AI prospect any time and get the same checklist score.
- Status
- In production with the client's agents
- Lives inside
- The client's learning platform, no separate sign in
Hosts and cleaners manage rental turnovers in chat, within each person's permissions
Owners, managers and cleaners all talk in one property chat. Now an AI there acts for each of them, only within that person's role.
- Status
- Live in the CleanHaus product
- Acts through
- CleanHaus APIs, under each user's own login
Compensation terms read from any physician contract, inside the client's own restricted cloud
Extraction code broke whenever a contract changed layout. One RAG pipeline now reads any contract, inside the client's own AWS account.
- Fields per contract
- 13, from provider to quality bonus
- Runs in
- The client's own HIPAA-aligned AWS account
What clients say
“You've always achieved everything that we've asked for, and it's amazing. The quality of your work is second to none, and we couldn't be more pleased.”
“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!”
5.0 average client rating on Upwork and Clutch
Brownfield AI · Why Datics
AI inside existing software is our specialty, not a side job.
We work on both the intelligence and the software it depends on.
AI and product engineering, together
We have built AI and data systems since 2018, alongside product engineering, so generative AI landed on skills we already had.
We fix what is underneath the AI
Most AI stalls on the product beneath it: architecture, APIs, data access and infrastructure. That is the part we change.
- In AI and data since
- 2018
- AI projects delivered
- 60+
- Ready-made AI modules
- 41
Partners, and the stack we build with
AWS
Select Tier Partner
Partner
Anthropic
Claude Partner Network
Partner
CopilotKit
AG-UI contributor
Contributor
LangGraph
Production agent engineering
We build with
Databricks
Governed AI & agent workflows
We build with
Microsoft Azure
Production AI delivery
We build with
Google Cloud
Production AI delivery
We build with
“My first question is never which model to use. It is what the users are trying to get done, and which parts of the product must not change.”
Umair Majeed · Co-founder & CEO“We have spent years inside codebases we did not write, with customers who could not be disrupted. That constraint is the job, not an exception.”
Rana Umar Majeed · Co-founder & CTOFor private equity: you set the AI priority across the portfolio, we build it inside each company's product.
See how we work with PE portfoliosInsights
Answers to what software leaders ask before adding AI.
Written by the people who build it: how we approach AI in a live product.
Automotive and dealerships
AI for car dealerships: where it pays in dealer software
Dealership AI earns its keep when every answer comes from real stock and every conversation ends in the CRM.
Umair Majeed · Nov 8, 2026AI-native
Which workflow should you make AI-native first?
The first AI workflow is not just a feature. It is the precedent every later one inherits.
Umair Majeed · Nov 7, 2026AI in production
How can prompt management put domain experts in control?
Engineers own the pipeline. Domain experts own what the AI says.
Rana Umar Majeed · Nov 3, 2026Your software already works. Now make AI work inside it.
No charge. You leave knowing where AI fits in your product and which start makes sense.