Automated color matching
individually calibrated per camera
for live broadcast.
Every multi-camera live production needs color matching. Every one still does it manually. That's the problem.
A global industry running on a manual, operator-dependent workflow that has not changed in decades.
Production quality should not depend on the experience of the technician. smart-matching changes that.
Results depend on operator skill
Identical hardware, different technicians — different results. Quality is non-reproducible. Rental companies can't guarantee output consistency when the operator changes.
40 minutes per 3-camera setup
An experienced video engineer needs 10–15 minutes per camera to manually adjust pedestal, gamma, white balance, chroma, and all remaining color parameters using waveform and vectorscope measurement. There is no automated alternative.
Senior specialists are scarce and expensive
The pool of technicians who can reliably match cameras to broadcast standard is small. Finding, booking, and paying them is a real operational constraint — especially for mid-market production companies.
No software solves it end-to-end
LUT-based tools shift the problem. Color grading systems (like Grass Valley Creative Grading) address post-production, not live calibration. No product automates the in-camera parameter loop directly via API.
Automated. API-direct.
Operator-independent.
smart-matching grabs the live video feed, samples a DSC Labs ChromaDuMonde or comparable industry-standard test chart, computes the delta to the reference values, and applies corrections directly via camera API — in a loop until convergence.
- On-premise. Runs locally on the production network. No cloud, no latency, no subscription dependency.
- API-direct correction. Writes values directly into cameras via manufacturer API. Vendor-neutral by design — no signal chain interference.
- Convergence loop. Correction runs until the measured delta falls below threshold — for each camera individually against the reference chart, not by transferring values from one camera to another. Not a one-shot adjustment.
- Cross-device. Different sensor sizes and manufacturer-specific color science make true vendor-neutrality a non-trivial technical challenge — which is exactly what makes the solution valuable.
| Feature | Manual | LUT / Post | smart-matching |
|---|---|---|---|
| Reproducible results | ✗ | ✗ | ✔ |
| Automated matching | ✗ | Partial | ✔ |
| No specialist required | ✗ | ✔ | ✔ |
| API-direct in camera | ✗ | ✗ | ✔ |
| Live production ready | ✔ | ✗ | ✔ |
Nobody has built it this way. Here is why.
The gatekeepers have no incentive
Camera manufacturers have had the necessary APIs for years. But no manufacturer has an incentive to build a tool that also matches competitors' cameras — true vendor-neutrality is structurally incompatible with their business model. Early manufacturer-native attempts therefore stay locked to their own camera fleet and copy reference values instead of calibrating each camera individually.
Live production has no second chance
LUT-based tools and color grading systems work on the recorded image — after the camera. In live broadcast there is no after. The image goes out live. These tools solve the wrong problem.
The timing is finally right
The right conditions are only now coming together: open APIs across almost all manufacturers, a team with deep knowledge of both the broadcast workflow and the technology to automate it, and a market that is growing faster than the available pool of specialists. The category is being defined now. The question is who is in.
Built on proven technology.
Before smart-matching, we built smart-reset — an open-source camera control tool used in live productions today. It proves that our core technology works in the field: reading live video signals, communicating directly with professional cameras, and analyzing color in real time. smart-matching takes exactly this foundation and adds the automation layer: it computes the color difference and corrects it without a human in the loop. 160+ installs in the field, ~2.4% organic penetration of the European SAM — with zero marketing spend, no paid promotion.
Browser-based interface for Panasonic and BirdDog PTZ cameras. Direct API control, no cloud, no subscription.
Live feed analysis. R, G, B channels visualized in BT.709 — the same color space smart-matching uses to compute the delta.
Saturation and hue trace in BT.709 with 75% color-bar targets — the reference tool technicians use to judge matching quality.
European beachhead.
Global standard.
Source: GrowthMarketReports 2024.
TV Studios
Streaming
OB Trucks
Recurring annual revenue.
| Segment | Europe Estimate | Priority |
|---|---|---|
| AV Rental & Production Companies | 1,800–2,400 | Y2 Entry |
| Streaming, Event & Podcast Studios | 1,200–1,500 | Y3 |
| News & TV Studios (3+ cameras) | 2,000–2,500 | Y3–Y4 |
| OB Trucks | 900–1,200 | Y4 |
Bottom-up estimates based on industry knowledge and publicly available data. Europe as initial target market. Global opportunity is structurally identical — same workflow, same problem, same solution.
Land → Expand → Scale.
Enter through rental companies where the pain is sharpest, then expand into studios and OB trucks.
AV Rental
Ambion GmbH + 1–2 peers. Beta deployments convert to paid Studio licenses. Direct outreach to the AV rental segment where operator-dependency costs are highest.
Studios & Streaming
Streaming and event studios, podcast studios. Direct outreach combined with ISE / IBC trade show presence to build pipeline in the studio segment.
OB & Enterprise
OB truck operators and TV studios. Enterprise tier launched. Reseller channel established for accelerated customer acquisition at scale.
Annual SaaS. Flexible options.
On-premise. No cloud dependency.
Recurring software licensing with service layers on top. Three SKUs designed for each distribution channel.
Systematically developed. Not a fluke.
Two people. One problem. We lived it before we built it.
The product is not built from a market study. It is built from 800+ productions worth of operational pain — and a developer who recognized the problem before it was explained.
Andreas has over 30 years of live production experience across broadcast, AV rental, and streaming environments. More than 800 productions delivered for clients including Audi, BMW, Deutsche Bank, and SAP. Built smart-reset (160+ installs) as the technical proof-of-concept for the stack underlying smart-matching.
LinkedIn →
Adil is a Fullstack developer and a passionate videographer and photographer. He recognized the problem the first time it was explained — not because he had researched it, but because he had experienced it himself. He joined with concrete solutions from day one.
LinkedIn →Ready to talk?
For investment inquiries, demos, and partnership discussions — send a brief note and we'll come back to you promptly.
Phone
Address
medien-support
Andreas Jaenisch
Simplonstr. 65
D-10245 Berlin
Germany
Investor questions,
answered directly.
The core stack — live signal capture, camera API control, and broadcast-grade scope analysis — is production-validated through smart-reset, which has been running in the field for two years. smart-matching automates the next step in that stack: the matching algorithm is in active development, with the MVP as the next milestone. This is not a concept on paper — it is an ongoing engineering project on a production-proven foundation.
The moat is not the code — it is the domain knowledge required to build it correctly. Getting the convergence loop to work reliably across different camera models, lighting conditions, and production environments requires deep broadcast expertise that cannot be replicated from a spec sheet. Additionally, the technical differentiation and direct integration into production infrastructure create high switching costs. A competitor starting today builds against a moving target.
LUTs apply color grades to the recorded image after the fact. smart-matching corrects the camera itself — via API, before the signal leaves the camera. The result is identical camera output in real time, not a color grade applied in post. For live broadcast, there is no "post" — the image goes out live.