Industries Workforce & HR Tech
Every hire is a decisionsomeone has to justify.
AI Engineering and software development for HR companies.
An applicant tracking system that ranks people is a regulated decision system in most of the markets you hire in. We build the screening, matching, and workforce intelligence underneath HR platforms and in-house teams, with the bias testing, explainability, and evidence trail that turn “the model said so” into an answer somebody can stand behind.
WeGenerateInternal Employee Payroll Integrated
The internal platform a pay-per-call sales operation runs on. Ringba call data reconciled against ad spend in one profit ledger, with employee payroll on the same record: commission, compensation and contracts per buyer.
- 28%
- Yearly revenue increase
- 15 min
- Ringba call data to live dashboards
- 100K
- Call records per ingestion batch
Why HR teamstrust us with delivery.
A screening model that inherits old hiring patterns does damage no apology walks back. This is why we pay special attention to the most sensitive parts of the system.
The training data is your own history
If the shortlist keeps looking like last year's hires, the model has learned your history, including the parts you were trying to change. Dropping protected fields is not enough, because postcode and career gaps carry them back in.
The AI Act named this category
The rules already landed: the AI Act's high-risk tier, New York's bias-audit law, Illinois' video-interview rules. Logging, human oversight, and candidate notice have to be designed in, not bolted on.
People data has the shortest fuse
A breach of salaries or performance notes is not a credential reset. It is colleagues reading what they were never meant to see, so access boundaries belong in the first schema.
Skills are the unit, not job titles
The same title means different work at two companies, so the question your team actually asks, who could step into this role, cannot be answered from titles. It takes a skills model built from messy free text.
Almost all of it is documents
Your pipeline runs on CVs in forty layouts, right-to-work evidence, contracts, and reference letters in a dozen languages. Extraction accuracy there decides whether anything built on top is worth trusting.
What HR demandsfrom engineering and AI.
01Matching that shows its reasoning
A ranked shortlist is only useful if a recruiter can see why someone is on it and challenge the answer. That takes scoring calibrated against real outcomes, who passed the interview and who was still there a year later, rather than how closely a CV echoes the job description.
02Screening at the speed of interest
Most drop-off in a hiring funnel is silence: days to a first reply, scheduling that takes a week, a status nobody updates. Screening throughput, automated scheduling, and honest progress messaging move conversion more reliably than anything added to the funnel's top.
03Internal mobility before external hiring
The cheapest qualified candidate is usually already on the payroll and invisible to the system. Surfacing them takes redeployment paths, ramp planning, and an honest read on how far someone's current skills sit from the open role, and it beats buying another sourcing licence.
04Analytics employees can live with
Attrition risk, capacity planning, and pay equity analysis are legitimate and valuable, and the same data can power monitoring nobody signed up for. Aggregation thresholds, what a manager sees about an individual, and what is deliberately never collected decide which one you built.
Real-life stories of triumph.
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Nortik not only delivered a solid foundation but their solution also helped us raise our first investment that same year. They feel less like an external partner but more like an in-house part of our team!
Nortik’s Impact
Enterprise-grade employee management solution with fully integrated SAP and Payroll services.
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Recent Posts
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Most AI prototypes stall somewhere between the demo and the deploy. The problem is rarely the model. It's the evals, guardrails, and infrastructure nobody scoped.
The eval suite is the product: testing LLM features like an engineer
Prompt changes without a test harness are blind bets. How we build eval suites from real traffic, and why they outlive every model you'll ever swap in.
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Token bills are the visible line item. The real costs hide in the architecture: retries, tool calls, human review loops. A breakdown from live systems.
Frequently asked questions
Everything people teams usually want to know before automating a hiring decision. Not here? Ask us directly.
If it screens applicants, allocates work, or informs promotion or termination, almost certainly. That is the high-risk tier, with obligations for risk management, data governance, logging, human oversight, and candidate notice.
We build to those requirements and produce the technical documentation that supports them. Classification and the legal filing are your counsel's call; our job is making sure the system can actually evidence what they sign.
Outcome-based testing against protected groups: selection rates, adverse impact ratios, and subgroup performance, run before launch and then on a schedule, because a model that passed in March can drift by September.
Where an independent audit is legally required, as under New York's Local Law 144, an external auditor performs it. We build the logging and reporting that makes their job possible rather than certifying our own work.
Yes, and the honest part of that answer is that layout variety and multilingual documents are where accuracy is won or lost, not the model choice.
We build extraction with confidence scoring, route the uncertain cases to a person instead of guessing, and measure accuracy against a labelled set that mirrors your real applicant mix rather than a clean benchmark.
Minimisation first: we design around the smallest set of fields that answers the question, with residency, retention, and deletion settled before the data model, and aggregation thresholds where individual-level output would be inappropriate.
Where a works council or employee representative body has a say, that consultation shapes the design rather than reviewing it at the end. It is far cheaper to build the constraint in than to argue it out after a pilot.
That's a large part of what we do: AI inside someone else's platform, built multi-tenant from the start, with per-tenant isolation, cost controls, and the audit surface your enterprise buyers' procurement teams will ask about.
In this category the compliance story is a sales asset. Bias reporting, documentation, and explainability are features your buyers evaluate, so we build them as product rather than as an appendix.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.