AI Opportunity Mapping
A focused engagement to find where AI is worth doing. We audit your workflows, data, and risk surface, then hand back a ranked roadmap, not a ninety-page deck.
- 2–4 weeks
- Fixed fee
APPLIED AI LAB Find the shape of your data.
Find the shape of your data.
Then put AI to work on it.
Kadmus is an applied AI lab. We help teams pinpoint where AI actually delivers value, then design, deploy, and secure the systems that get it there.
- Senior engineers from finance, security, and cloud, not slideware.
- Security and compliance designed in from the first commit.
- Working software in weeks, with value you can measure.
SERVICES
What we do
Four ways we engage. We start small: one scoped problem with a clear measure of value, then expand only when the work earns it.
Applied AI & Automation
Custom LLM and agent workflows built around how your team actually works. Retrieval, evaluation, and guardrails included, so it holds up once it meets real data.
- 8–16 weeks
- Embedded team
Secure & Compliant Deployment
We design deployment boundaries, access controls, logging and traceability around your security requirements, including architectures mapped to SOC 2 and ISO 27001 controls. Where required, sensitive data stays within approved infrastructure and data boundaries.
- Ongoing
- Project-based
Data & Analytics Engineering
The pipelines, warehouses, and reporting that make AI possible, and useful, in the first place. Clean inputs are most of the battle.
- 4–12 weeks
INDUSTRIES
The model is general. The work isn't.
The workflows, systems, terminology, constraints and failure modes differ by industry. We build around that operating context.
Finance & Asset Management
Pre-trade guideline review, post-trade exceptions, reconciliation and client reporting, run against your mandates, holdings and reference data inside infrastructure you approve.
Insurance
Submissions, endorsements and claims files assembled from your policy wording and supporting evidence, with the clauses and facts behind each decision surfaced for the professional making it.
Manufacturing & Industrial Operations
Quality, maintenance and production exceptions resolved against the BOM, the routing and the work order that are current, not the revision that happened to be printed.
Legal for Small & Mid-sized Firms
Intake, conflicts, calendaring, discovery preparation and billing worked against the matters and rules your firm already operates under, with the responsible lawyer reviewing anything consequential.
OUR PHILOSOPHY
Models commoditize. Context doesn't.
A frontier model can pass the bar exam and write production code, and still not know your pricing exceptions, your escalation paths, or the judgment your best people use to make a call. If you and your competitor call the same API, the model is nobody's advantage; it's a shared utility, like electricity.
The edge that lasts is the context a company runs on: its documents, systems of record, and rules. It's yours alone, it took years to accumulate, and it can't be downloaded. We build the private layer that turns that knowledge into AI you can trust, grounded, cited, and run on infrastructure you control.
And we don't stop at the demo. Most pilots fail when the model meets messy data and workflows no one documented, so we work like forward-deployed engineers: embedded in your environment, owning the path to production, and staying until your team can run it without us.
95%
MIT NANDA's 2025 report found that 95% of the generative-AI deployments it studied showed no measurable return. It drew on 300 publicly disclosed deployments alongside interviews and survey responses.
The methodology is debated, and we think the headline number is softer than it reads. We cite it for the pattern it describes, not the precision of the figure.
HOW WE WORK
Narrow, fast, and built to last
01
Start narrow
One workflow, clearly scoped, where the value is measurable from day one.
02
Build in the open
Short cycles and working software you can try every week, no black boxes.
03
Earn production
Evaluation, monitoring, and security review before anything goes live.
04
Hand it over
Documentation and training, so your team can run it with or without us.
ABOUT
A deeply technical team with a complementary skillset
We met in the trenches of finance, security, and cloud. We started Kadmus because too many good AI ideas die somewhere between the demo and production. We build the part that survives.
Philip Basaric
Quant developer at CC&L, building post-trade analysis systems for quantitative equities, after data engineering across cybersecurity and privacy at PwC. He owns Kadmus's data and modeling backbone.
Julien Gou
Information security lead at S&P Global across application, cloud, and AI security, with earlier stints in security consulting at Accenture and security operations at PwC. He owns how we deploy safely in regulated environments.
Ujjwal Kumar
Spent four years as a software engineer at AWS building managed Apache Airflow, distributed systems and data infrastructure at scale, with earlier engineering at SAP and machine-learning research at McGill's Prometheus AI lab. He leads the engineering that takes a prototype to production.
CONTACT
Tell us what you're trying to build.
Early conversations are free and low-pressure. If AI isn't the right tool for the job, we'll say so.