For DeepTech startups.
We turn your technical inputs into reliable investor-ready financial models.
We turn your technical inputs into reliable investor-ready financial models.
Be well prepared for your next move. We help you scale.
From Engineering-Ready to Investor-Ready
- we built over 50 financial models for DeepTech sector (including AI, drones, PV Solar, BESS, Wind, Biomethane, composite recycling plants)
- our clients are in Italy, Switzerland, France, Germany, Cyprus, Greece, Spain, Finland, Norway, USA, Bermuda
- we are expanding across Europe
- financial model in 3-4 weeks
- fundraising investor deck in 1 week
- fixed price official contracts
- CFO further retainer to scale your business
When teams bring us in
Most clients do not come to us because they “need a model.” They come to us because a real financing or scrutiny event is close, and the numbers need to hold up
- You have an MVP ready and have first revenue, but your current model is not fit for investors to scale?
- Raising an equity round? You need a model, valuation, and use-of-funds story investors can diligence without pulling apart your spreadsheet.
- Seeking project finance or debt? You need debt sizing, covenant logic, and cash timing that stand up under downside questions.
- Preparing for investor diligence? You need the assumptions, outputs, and narrative to reconcile before outsiders start pressure-testing the case.
- Building the investable model? You need to turn a plant, portfolio, or technically complex rollout into a financeable case with clear economics and milestones.
Some of our cases
Get a full control in your capital conversations. We are here to translate your vision into numbers and speak the same language with your potential investors.
We built over 50 complex financial models for DeepTech companies across Europe and USA up to investment-grade standards, including: 3-Statement Model, DCF, M&A, LBO, project models, and other.
We calculate project economics, so you can clearly understand your key parameters, monitor their dynamics, and manage them effectively.
We design not only top-down valuation models but also complex bottom-up dynamic financial models that become a copilot for your business operations, helping you identify the right mix of products and sales channels to maximize and stabilize profits.
We are here to help you scale – we also provide CFO services for selected companies.
We built over 50 complex financial models for DeepTech companies across Europe and USA up to investment-grade standards, including: 3-Statement Model, DCF, M&A, LBO, project models, and other.
We calculate project economics, so you can clearly understand your key parameters, monitor their dynamics, and manage them effectively.
We design not only top-down valuation models but also complex bottom-up dynamic financial models that become a copilot for your business operations, helping you identify the right mix of products and sales channels to maximize and stabilize profits.
We are here to help you scale – we also provide CFO services for selected companies.
Biomethane | Multiple Plants | €25–30m per plant | Project Models, USA, Italy
Biomethane - biogas plants | Multiple Projects | €25–30m per plant. A biogas portfolio of several plants. The sponsor needed an investor-ready financial model that could scale correctly across projects, incorporate government incentives and debt financing, and support fundraising.
What was wrong before:
The existing model did not scale properly across plants, contained CAPEX discrepancies, and relied on weak operating drivers. It also lacked robust treatment of incentives, debt financing, tax calculations, SPV structure, and equity waterfall logic.
What we delivered:
A rebuilt integrated model covering plant-level assumptions, corrected CAPEX and operating drivers, incentive mechanics, debt structuring, tax calculations, SPV-level forecasting, and equity waterfall sections.
Outcome:
We delivered a bankable, investor-grade model that helped the sponsor secure investor funding. Two plants have already been built.
What was wrong before:
The existing model did not scale properly across plants, contained CAPEX discrepancies, and relied on weak operating drivers. It also lacked robust treatment of incentives, debt financing, tax calculations, SPV structure, and equity waterfall logic.
What we delivered:
A rebuilt integrated model covering plant-level assumptions, corrected CAPEX and operating drivers, incentive mechanics, debt structuring, tax calculations, SPV-level forecasting, and equity waterfall sections.
Outcome:
We delivered a bankable, investor-grade model that helped the sponsor secure investor funding. Two plants have already been built.
Solar PV, 40 MWp | Fundraising Models for a Founder-Led EPC in Italy, Cyprus, Bermuda
A late-stage greenfield solar PV project with a 20-year PPA/offtake, expected COD after financing, and a full data room including EPC, interconnection, independent engineering, and a lender term sheet needed an investor-ready model to assess bid value, debt sizing, and sponsor returns. The asset was structured as 25 MWdc / 20 MWac with a 30-year life and production cases built around P50 / P75 / P90 energy yield.
What was wrong before:
Key commercial, technical, and financing inputs sat in separate workstreams. There was no integrated view of COD, PPA pricing, merchant tail, degradation, CAPEX build-up, DSRA, DSCR covenant headroom, or tax / depreciation treatment, which made it difficult to underwrite downside cases or size the capital stack with confidence.
What the sprint delivered:
An integrated solar project finance model covering MWdc / MWac sizing, PPA revenue, post-PPA pricing, production and degradation, EPC and interconnection costs, O&M, reserve accounts, debt sizing, taxes, levered and unlevered IRR, and sensitivity analysis across PPA price, production, capital costs, and financing terms. Outputs included sources & uses, cash flow, covenant testing, and lender / investor-ready charts.
Outcome:
We delivered a bankable underwriting model that allowed the sponsor to test leverage, benchmark returns against hurdle-rate and lender thresholds, and move into a investment decision with a clear view of the project’s risk and return profile.
What was wrong before:
Key commercial, technical, and financing inputs sat in separate workstreams. There was no integrated view of COD, PPA pricing, merchant tail, degradation, CAPEX build-up, DSRA, DSCR covenant headroom, or tax / depreciation treatment, which made it difficult to underwrite downside cases or size the capital stack with confidence.
What the sprint delivered:
An integrated solar project finance model covering MWdc / MWac sizing, PPA revenue, post-PPA pricing, production and degradation, EPC and interconnection costs, O&M, reserve accounts, debt sizing, taxes, levered and unlevered IRR, and sensitivity analysis across PPA price, production, capital costs, and financing terms. Outputs included sources & uses, cash flow, covenant testing, and lender / investor-ready charts.
Outcome:
We delivered a bankable underwriting model that allowed the sponsor to test leverage, benchmark returns against hurdle-rate and lender thresholds, and move into a investment decision with a clear view of the project’s risk and return profile.
BESS, Switzerland | Investor-Ready Model
Small EPC and O&M team evaluating its first serious storage growth move. The team needed a model that could compare merchant, floor, and contracted revenue cases.
What was wrong before:
The existing file had rough revenue assumptions but no serious treatment of degradation, augmentation, availability, reserve accounts, or funding structure.
What the sprint delivered:
A scenario-based BESS model with revenue cases, up&downside sensitivities, funding logic, and lender PE investor outputs.
Outcome:
The sponsor ruled out a weaker commercial path early, focused on the more defensible structure, and used the model to frame discussions with financing and revenue counterparties.
What was wrong before:
The existing file had rough revenue assumptions but no serious treatment of degradation, augmentation, availability, reserve accounts, or funding structure.
What the sprint delivered:
A scenario-based BESS model with revenue cases, up&downside sensitivities, funding logic, and lender PE investor outputs.
Outcome:
The sponsor ruled out a weaker commercial path early, focused on the more defensible structure, and used the model to frame discussions with financing and revenue counterparties.
Wind Power, Bermuda, Italy | Fundraising model
Small private wind opportunity with founder-led sponsorship. The team needed to test whether the project economics still worked after FX, logistics, insurance, and downside assumptions.
What was wrong before:
The initial model was too optimistic and did not properly account for import exposure, contingency, downtime risk, or covenant stress.
What the sprint delivered:
A fundraising model with FX sensitivities, downside cases, sponsor funding logic, and clear outputs for investor discussion.
Outcome:
The sponsor adjusted capital structure assumptions before approaching investors and avoided taking an under-defended case to market.
What was wrong before:
The initial model was too optimistic and did not properly account for import exposure, contingency, downtime risk, or covenant stress.
What the sprint delivered:
A fundraising model with FX sensitivities, downside cases, sponsor funding logic, and clear outputs for investor discussion.
Outcome:
The sponsor adjusted capital structure assumptions before approaching investors and avoided taking an under-defended case to market.
Plant in recycling of composites, Switzerland. Integrated bottom-up operational & fundraising model
Founder-led, a Swiss company in recycling of composites, needed a detailed bottom-up operating model for its MVP plant and a scalable template for next plants in Europe. A critical part of the work was modeling three revenue streams in one coherent structure: recycling income, thermolysis oil sales, and reclaimed fiber sales to downstream manufacturing customers in sectors such as boating and automotive.
What was wrong before:
The existing economics were too high-level for fundraising and did not fully capture plant-level operating drivers, output yields, or the interaction between recycling revenues and downstream product sales.
What was delivered:
A detailed bottom-up operational and financial model for the current plant, built to scale across future facilities. The model integrated throughput, recovery yields, OPEX, CAPEX, three revenue streams, and investor-ready outputs for management and fundraising use.
Outcome:
A clear investor-grade model that the founder used to raise the financing needed, while also creating a reusable modeling framework for the European rollout.
What was wrong before:
The existing economics were too high-level for fundraising and did not fully capture plant-level operating drivers, output yields, or the interaction between recycling revenues and downstream product sales.
What was delivered:
A detailed bottom-up operational and financial model for the current plant, built to scale across future facilities. The model integrated throughput, recovery yields, OPEX, CAPEX, three revenue streams, and investor-ready outputs for management and fundraising use.
Outcome:
A clear investor-grade model that the founder used to raise the financing needed, while also creating a reusable modeling framework for the European rollout.
DeepTech (AI/ SaaS Logistics Platform for Drone Operations), Germany | Financial Model & Valuation
Founder-led AI SaaS logistics platform for autonomous drone operations combining proprietary flight-control software, in-house R&D and production needed an investor-grade financial architecture to support fundraising and scaling.
What was wrong before: An internal model existed but lacked integrity across revenues, R&D, production, and logistics operations. It did not include valuation logic, WACC, ARR and churn metrics, or unit-economics analysis by route. Forecasts were difficult to reconcile with ERP accounting numbers, and maintaining consistent versions in the dataroom became increasingly difficult as investor questions grew.
What we delivered: A fully integrated model covering ARR build-up, churn, CAC/LTV, route-level unit economics, and scenario analysis for commercialization. R&D spending was structured with capitalization under IAS 38 (IFRS), and the model included valuation, WACC, and sensitivity of valuation to growth, margin, and risk assumptions.
Outcome: The founder moved from defending spreadsheets to controlling the narrative with faster Q&A, clean dataroom management, and a model that stayed current as accounting data updated. As a result, they unlocked PE financing.
What was wrong before: An internal model existed but lacked integrity across revenues, R&D, production, and logistics operations. It did not include valuation logic, WACC, ARR and churn metrics, or unit-economics analysis by route. Forecasts were difficult to reconcile with ERP accounting numbers, and maintaining consistent versions in the dataroom became increasingly difficult as investor questions grew.
What we delivered: A fully integrated model covering ARR build-up, churn, CAC/LTV, route-level unit economics, and scenario analysis for commercialization. R&D spending was structured with capitalization under IAS 38 (IFRS), and the model included valuation, WACC, and sensitivity of valuation to growth, margin, and risk assumptions.
Outcome: The founder moved from defending spreadsheets to controlling the narrative with faster Q&A, clean dataroom management, and a model that stayed current as accounting data updated. As a result, they unlocked PE financing.
AI/ SaaS Longevity MedTech Platform for preventive monitoring, Italy
Founder-led AI-based SaaS startup, needed CFO services to preserve their runway, establish ERP system and budgeting. We started with preparing a complete pack with several financial models for M&A, fundraising and subsequent operations in Europe.
What was wrong before: Only internal Excel files existed, built for operational use rather than institutional fundraising. They did not translate the business into core SaaS and MedTech metrics. They also could not support transaction work - no valuation logic, no merger mechanics, no post-merger integration view, and no dedicated fundraising model tied to use of proceeds.
What we delivered: Our model included cohort retention, CAC by each acquisition channel, LTV, payback period, recurring vs non-recurring revenue mix, gross margin by service layer, scaling efficiency by site type.
Outcome: The company moved from internal spreadsheets to a PE-grade capital-raising package that explained not just growth, but the quality of growth. The result was a model founders could defend, investors could diligence, and the company raised private equity capital.
What was wrong before: Only internal Excel files existed, built for operational use rather than institutional fundraising. They did not translate the business into core SaaS and MedTech metrics. They also could not support transaction work - no valuation logic, no merger mechanics, no post-merger integration view, and no dedicated fundraising model tied to use of proceeds.
What we delivered: Our model included cohort retention, CAC by each acquisition channel, LTV, payback period, recurring vs non-recurring revenue mix, gross margin by service layer, scaling efficiency by site type.
Outcome: The company moved from internal spreadsheets to a PE-grade capital-raising package that explained not just growth, but the quality of growth. The result was a model founders could defend, investors could diligence, and the company raised private equity capital.
AI/ SaaS EdTech, Italy | AI-based Career Development Platform | Financial Model & Fundraising Pack
Founder-led AI EdTech platform focused on career development, skills mapping, and employability pathways needed an investor-grade financial model to support fundraising and scaling.
What was wrong before: Internal spreadsheets existed, but they did not translate the business into core SaaS and EdTech metrics. There was no clear logic for ARR build-up, churn, CAC by acquisition channel, or revenue split between recurring subscriptions and non-recurring services. The model also lacked valuation logic and a clear view of how commercialization pace affected runway and funding needs.
What we delivered: A fully integrated model covering ARR, churn, CAC/LTV, cohort retention, payback, gross margin by service layer, and unit economics by learner / enterprise client segment. We added scenario analysis for commercialization pace, cash runway, fundraising needs, and sensitivity of valuation to growth, margin, and risk assumptions.
Outcome: The founder moved from generic planning files to an investor-ready finance pack that made the growth story measurable, defensible, and easier to explain in fundraising discussions.
What was wrong before: Internal spreadsheets existed, but they did not translate the business into core SaaS and EdTech metrics. There was no clear logic for ARR build-up, churn, CAC by acquisition channel, or revenue split between recurring subscriptions and non-recurring services. The model also lacked valuation logic and a clear view of how commercialization pace affected runway and funding needs.
What we delivered: A fully integrated model covering ARR, churn, CAC/LTV, cohort retention, payback, gross margin by service layer, and unit economics by learner / enterprise client segment. We added scenario analysis for commercialization pace, cash runway, fundraising needs, and sensitivity of valuation to growth, margin, and risk assumptions.
Outcome: The founder moved from generic planning files to an investor-ready finance pack that made the growth story measurable, defensible, and easier to explain in fundraising discussions.
Book a 30' fit call. You can come with a broken internal model, partial project data, or a blank sheet
What has to be true before you go out to investors or lenders
Check, if your current model has a hypothetical "top down" logic or is anchored in contract-based "bottom up" approach FinburgH mainly uses. Usually in-house models do not reflect the language the investors speak and it's hard for you to explain:
- How the model ties to real technical and commercial drivers
- Valuation logic and WACC calculation
- Inventory DSO forecasted / working capital
- Debt sizing or CapTable
- Complex R&D issues
- Up & downside cases and sensitivity analysis
- Use-of-funds logic
- Complete schedules for all parts of 3-Statement model
- Prove that the numbers and the story actually match
If even one of these is weak, the conversation shifts from momentum to doubt
What we deliver to get that job done
- High Quality
- Investor-grade valuation models or fundraising models or project models
- Valuation and WACC calculation based on comparables
- 3 scenario cases, sensitivity analysis
- Clear defendable assumptions and investor-ready outputs
- Concise pitch deck for investors/ Board deck that corresponds the model
- Trusted CFO partner before investor review
- High professional standardsWe follow IVS (international valuation standards), all our analysts are professionally trained, certified and use FMVA / CFA methodology for the models we build
- Tailored Approach - we build each pack and each model EXCLUSIVELY for youYour business is unique and deserves a tailored approach. You will have a designated senior financial analyst who is responsible for your model and a CFO review before each release.
Book a 30' fit call
How the sprint works?
We always work under an official contract - our office is based in Milan, Italy and we work across Europe
You call or write to WhatsApp
You call or write to WhatsApp
We schedule a 30' fit call
At the call
At the call
We discuss your business need, determine the scope
Within 48 hrs
Within 48 hrs
We send you a proposed Scope of Work, initial data / assumptions requirements, NDA contract
Contract signature & 1st payment
Contract signature & 1st payment
Sign-off on the initial data / assumptions/ drivers/ model structure
Model Review Session & 2nd payment
Model Review Session & 2nd payment
Minor adjustments within the SOW
3rd payment
3rd payment
Complete pack, final delivery
Prices are within the market range. You do not want to look underprepared when investors, lenders, or partners start digging into the numbers. Get in touch!