Bordeciel
We capture data in markets where data is hard to get. We interpret it for your needs. You get clean, custom solutions.
Manifesto
Every market throws off more signal than the people inside it can hold. Bordeciel builds systems that ingest that signal, resolve it into structure, and return decisions the client can act on. What we ship changes by client. The operation underneath does not.
Underneath, the method is mathematical. Statistics, information theory, and applied inference, tools that measure their own uncertainty. Every model runs against a baseline. Every claim carries an error bar. That is what "engineered" means when we say it.
Services
Two services. ◆ Valuence prices creator content so that you can negotiate brand deals based on expected results, instead of gut feeling. ◆ Data Engine gives you visibility on your market, ingests the sources that make it specific, and ships the operational systems your team needs close the loop. Each solving real pains entire organizations used to feel.
Valuence, content valuation for creator negotiations
Valuence answers one question: how do you price a brand deal with a creator? Live engagement, audience quality, and cadence signal from Instagram, YouTube, and TikTok run through a statistical model and come out as a number both sides of the room can point at. Creators use it to know their worth. Agencies use it to close, and to defend the number in every room.
- creator content valuation · fresh report + deal simulation on any creator
- budget efficiency analysis · flag overspend, recommend the corrective move
- negotiation intelligence · enforceable framework, ROAS uplift on creator spend
Data Engine, market visibility and operational systems
Data Engine gives you visibility on markets where data is hard to get. Whether it is lead generation or full-on market monitoring, we scan your niche sector at regional or country scale at the frequency you need. Where the public data ends, we build the bespoke sources and signals your market's quirks demand. Where operations need a surface, we ship the CRM, dashboards, and mobile apps that close the loop, and we run them in production. Every deployment widens the base layer the next one runs on.
- market intelligence · niche sector scanned at regional or country scale
- bespoke data sources & signals · we ingest what your market actually runs on
- all-in-one systems · CRM, dashboards, mobile apps, managed in production
All-in-one systems (reference build)
This is where the engine meets the field. For a beauty conglomerate client we built the operating environment its field team actually opens: a mobile-first surface where prospect ranking, territory potential, and market segmentation from the engine reach the operator on the road, and where every field interaction feeds back as ground-truth. Some verticals still lack software that matches how they operate; we build it, and it becomes both the interface layer for the engine's outputs and the first-party sensor that keeps the engine honest.
Methodology
We build systems the way experiments are run, and we build the engine the way instruments get more accurate: one deployment at a time, against a shared base layer.
how we build systems
Every model has a baseline. Every deployment has a control. Every version is measured against the last. Every claim ships with its confidence score. When the environment changes, the system adapts and delivers consistency.
how the engine compounds
Every vertical deployment feeds the base layer. Signal captured for one client resolves into structure the next client's model runs against. Field ground-truth from an operating team calibrates what the scrapers see the following week. The engine gets sharper every time it is used, in a way any single service, on its own, could not.
who reads the engine
Public markets are measured tick by tick. The private economy, the businesses that make up the majority of employment and output, is not. What the engine's base layer accumulates, across every vertical it is deployed in, will eventually be read by the institutions that need it most. Financial institutions ingesting it as signal for models built on the private sector. Public institutions writing policy in tune with the businesses it affects. We are not there yet. The path there is one vertical at a time.
Founders
Steven, Infrastructure
Quant infrastructure development at RBC and Morgan Stanley, implementing sophisticated models that need to survive production under heavy load. Builds complex systems where operational reliability is indispensable.
Celestino, Realtime
Engineered real-time systems at Microsoft. Reached Microsoft AI via the Nuance Labs acquisition, on the Innovation Team building healthcare AI under HIPAA. Designed clean systems where constraints are the hardest: regulated data, real-time streams and life-critical decisions.
Baptiste, Innovation
Architected and built the full-stack system that runs L'Oréal's field operations in Canada, live in production for years. Finance and mathematics background applied to product research and development.
Clients
- multinationals, category leaders instrumenting their market
- agencies, defending a number in every room
- financial institutions, private-sector signal, into the model
- consulting firms, analysis they cannot produce by hand
- government institutions, evidence for the public record
- private capital, opportunity mapping at fund scale
names withheld by default.
Security & compliance
We build for enterprise clients and hold ourselves to their bar. Reports available on request.
- SOC 2 · security compliance report
- ISO 27001
- GDPR
- CyberGRX (ProcessUnity Exchange)
Contact
Fortune 500 clients trust our services. We are confident we can satisfy your needs too. Tell us the shape of the problem. We respond within two business days.