Where we can help
Examples of business challenges where we help create value: new products and defensible IP, measurable financial results, and AI transformation.
Invent what’s next
New products and defensible IP.
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AI-accelerated R&D
- The problem
- Expert time is scarce, and the innovation funnel is slow and opaque: leadership cannot see which projects are progressing, stalling or duplicating effort, so portfolio decisions arrive late and rest on instinct.
- What we build
- AI sits inside the actual workflow: scientific literature and patent mining, experiment and simulation support, automated documentation. A transparent funnel view lets the Technology Board steer on evidence.
- The payoff
- Researchers spend more time on invention. Projects get killed or accelerated months earlier, based on what the funnel actually shows.
Tech evaluation & IP strategy
- The problem
- Technology bets are made under a flood of ‘AI-powered’ pitches, without a clear read on the IP landscape: freedom-to-operate risk, where your patentable advantage lies, and which vendor claims hold up.
- What we build
- We assess each technology’s maturity, defensibility and fit with your roadmap, and give you a clear buy, build or partner call. Alongside it: a freedom-to-operate map and a position on what to patent and what to publish.
- The payoff
- Capital goes to bets that survive scrutiny. You build your IP position early, and you stay free of vendor lock-in.
AI-accelerated R&D
- The problem
- Expert time is scarce, and the innovation funnel is slow and opaque: leadership cannot see which projects are progressing, stalling or duplicating effort, so portfolio decisions arrive late and rest on instinct.
- What we build
- AI sits inside the actual workflow: scientific literature and patent mining, experiment and simulation support, automated documentation. A transparent funnel view lets the Technology Board steer on evidence.
- The payoff
- Researchers spend more time on invention. Projects get killed or accelerated months earlier, based on what the funnel actually shows.
Tech evaluation & IP strategy
- The problem
- Technology bets are made under a flood of ‘AI-powered’ pitches, without a clear read on the IP landscape: freedom-to-operate risk, where your patentable advantage lies, and which vendor claims hold up.
- What we build
- We assess each technology’s maturity, defensibility and fit with your roadmap, and give you a clear buy, build or partner call. Alongside it: a freedom-to-operate map and a position on what to patent and what to publish.
- The payoff
- Capital goes to bets that survive scrutiny. You build your IP position early, and you stay free of vendor lock-in.
Consolidate value
Finance is where AI pays back first: margin, reporting, procure-to-pay and cash. Each model is built on your own data, which is what makes it hard to copy.
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Margin intelligence
- The problem
- Pricing, discounting and service decisions run on gut feel, because the real profit and loss sits one level below anything the ERP shows.
- What we build
- The model joins the transactional, operational and cost-to-serve data your ERP keeps in silos, and shows where margin is made and lost, account by account and product by product.
- The payoff
- Recovered margin from repricing, renegotiating or exiting loss-making accounts. Often the fastest payback we deliver.
Regulated reporting
- The problem
- Regulated reporting (IFRS 17, Swiss Solvency Test, FINMA-facing disclosures) costs too much and takes too long. Generic AI tools make it faster but produce black-box outputs that auditors will not accept without a clear trail.
- What we build
- AI accelerates preparation and reconciliation, and a governance layer records how every figure was produced: source data, transformations, and the reasoning behind each adjustment.
- The payoff
- Lower cost and faster cycles on reporting that must happen anyway. Each AI-assisted figure comes with the trail behind it.
Procure-to-pay
- The problem
- Purchase orders, goods receipts and invoices sit across systems and spreadsheets; exceptions like price mismatches, duplicates and invoices with no receipt surface only after they become disputes or write-offs.
- What we build
- AI reconstructs the three-way match in real time and proposes GL coding learned from your own historical postings, built into your existing ERP rather than replacing it.
- The payoff
- Most routine invoices processed without manual touches, with a complete audit trail. You typically see the first results in the first cycle: a cleaner exception queue, fewer duplicate payments.
Cash & working capital
- The problem
- Cash forecasts live in spreadsheets and go out of date within days, and group-level DSO, DPO and DIO averages hide where liquidity is actually stuck.
- What we build
- A rolling, short-horizon forecast built directly from transactional data, with working capital broken down by customer, supplier and entity.
- The payoff
- Earlier warning when liquidity tightens, and action on the specific accounts that matter instead of blanket group policies.
Margin intelligence
- The problem
- Pricing, discounting and service decisions run on gut feel, because the real profit and loss sits one level below anything the ERP shows.
- What we build
- The model joins the transactional, operational and cost-to-serve data your ERP keeps in silos, and shows where margin is made and lost, account by account and product by product.
- The payoff
- Recovered margin from repricing, renegotiating or exiting loss-making accounts. Often the fastest payback we deliver.
Regulated reporting
- The problem
- Regulated reporting (IFRS 17, Swiss Solvency Test, FINMA-facing disclosures) costs too much and takes too long. Generic AI tools make it faster but produce black-box outputs that auditors will not accept without a clear trail.
- What we build
- AI accelerates preparation and reconciliation, and a governance layer records how every figure was produced: source data, transformations, and the reasoning behind each adjustment.
- The payoff
- Lower cost and faster cycles on reporting that must happen anyway. Each AI-assisted figure comes with the trail behind it.
Procure-to-pay
- The problem
- Purchase orders, goods receipts and invoices sit across systems and spreadsheets; exceptions like price mismatches, duplicates and invoices with no receipt surface only after they become disputes or write-offs.
- What we build
- AI reconstructs the three-way match in real time and proposes GL coding learned from your own historical postings, built into your existing ERP rather than replacing it.
- The payoff
- Most routine invoices processed without manual touches, with a complete audit trail. You typically see the first results in the first cycle: a cleaner exception queue, fewer duplicate payments.
Cash & working capital
- The problem
- Cash forecasts live in spreadsheets and go out of date within days, and group-level DSO, DPO and DIO averages hide where liquidity is actually stuck.
- What we build
- A rolling, short-horizon forecast built directly from transactional data, with working capital broken down by customer, supplier and entity.
- The payoff
- Earlier warning when liquidity tightens, and action on the specific accounts that matter instead of blanket group policies.
AI transformation
AI that people keep using, infrastructure you own, and rules you can defend to a regulator.
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AI sovereignty
- The problem
- Most enterprises inherit their AI dependencies instead of choosing them: data sits where the vendor puts it, models run that nobody can inspect, and know-how flows upstream with every prompt.
- What we build
- We map your position across the four dimensions that decide sovereignty: data, model, knowledge and exit, with jurisdiction behind each. Then we close the gaps that matter, using tools like a self-hosted AI gateway, in-house models and contracts designed for exit.
- The payoff
- Every dependency chosen, priced and exitable. And when a board or a regulator asks what happens if a key vendor leaves tomorrow, you have the answer.
GenAI adoption that lasts
- The problem
- The common pattern: enthusiasm at launch, then usage decaying to a fraction of licensed seats. Citizen automation without rules adds fragile, unowned flows and agents on top.
- What we build
- Adoption run as a programme: role-specific enablement, measured usage, help where it stalls. Citizen development gets guardrails, ownership rules and a registry, so what people build is owned and maintained.
- The payoff
- Usage that holds instead of decaying, automations that stay maintained, and a measurable return on licence spend.
AI governance & EU AI Act
- The problem
- Few companies can say what AI they run: models, vendors, embedded features and citizen-built agents accumulate without an inventory, while the EU AI Act’s obligations arrive on fixed dates.
- What we build
- An AI registry as the foundation: what is deployed, where, on what data, at what risk level. Then classification against the Act’s risk categories, and an operating model for the decisions: who decides, how it is steered, what the board sees.
- The payoff
- Compliance work done at normal cost instead of crisis cost, and rules clear enough that adoption speeds up instead of stalling.
AI sovereignty
- The problem
- Most enterprises inherit their AI dependencies instead of choosing them: data sits where the vendor puts it, models run that nobody can inspect, and know-how flows upstream with every prompt.
- What we build
- We map your position across the four dimensions that decide sovereignty: data, model, knowledge and exit, with jurisdiction behind each. Then we close the gaps that matter, using tools like a self-hosted AI gateway, in-house models and contracts designed for exit.
- The payoff
- Every dependency chosen, priced and exitable. And when a board or a regulator asks what happens if a key vendor leaves tomorrow, you have the answer.
GenAI adoption that lasts
- The problem
- The common pattern: enthusiasm at launch, then usage decaying to a fraction of licensed seats. Citizen automation without rules adds fragile, unowned flows and agents on top.
- What we build
- Adoption run as a programme: role-specific enablement, measured usage, help where it stalls. Citizen development gets guardrails, ownership rules and a registry, so what people build is owned and maintained.
- The payoff
- Usage that holds instead of decaying, automations that stay maintained, and a measurable return on licence spend.
AI governance & EU AI Act
- The problem
- Few companies can say what AI they run: models, vendors, embedded features and citizen-built agents accumulate without an inventory, while the EU AI Act’s obligations arrive on fixed dates.
- What we build
- An AI registry as the foundation: what is deployed, where, on what data, at what risk level. Then classification against the Act’s risk categories, and an operating model for the decisions: who decides, how it is steered, what the board sees.
- The payoff
- Compliance work done at normal cost instead of crisis cost, and rules clear enough that adoption speeds up instead of stalling.
The pattern behind the work
We redesign the operating model so the tools earn their place. We build governance in early, so adoption can scale safely. And we transfer the capability, so the results survive us.
Hintelia
You bring the ambition; we bring the structure.