The same signal engine, four kinds of work: research the market, answer with an auditable memory, enrich contacts and accounts, build the GTM infrastructure that runs it all.
The radar audits market context every day. Every source enters with a unique fingerprint, so reading it again never creates a second copy and duplicates get blocked. The output is verified business signals: investment rounds, job openings, leadership changes, without repeated noise.
The Single Signal Layer doubles as a defensive radar for reputational risk: information anomalies created by state actors or coordinated networks. The Cross-Outlet X-Ray Filter compares the text of each story against the others and locates the same content spread artificially across different outlets, isolating fabricated noise.
When evaluating complex investments or acquisitions, the engine traces the affiliation of opaque corporate networks and their subsidiaries. It also cross-references each domain against the company's real tax identity, its status in Argentina's ARCA/AFIP registry, to surface audit risk without manual legwork.
The system audits unregulated markets, such as online casinos, identifying hidden corporate networks and alternative payment-gateway integrations. It raises alerts when mirror domains rotate or short-lived infrastructure appears, and flags mass traffic migrations or anomalous transaction spikes.
Instead of starting from a purchased list, the enrichment engine builds its own census of companies by vertical (software, real estate and legal, among others) with tax IDs verified against the real fiscal registry. That gives an auditable prospecting universe, not a purchased database of unknown origin.
Each account's context stays unified across two layers: the Synthesized Wiki, with market context in weekly batches, and the Account Lore, with the daily read of who decides and what is known inside. When answers or recommendations are generated, every claim is checked against the original text, and if it does not match, the whole read is discarded.
When the engine detects buying intent or a significant change in the software an account uses, it does not reply to the client: it prepares the context, the structured read and the message package for each channel. Anything that proposes a message is born as a held draft and needs a person's explicit approval before it goes out.
We deploy digital teams trained only on the client's own business context, handling bookings, lead qualification and inbound calls. The goal is to run smoothly in sectors like home services, e-commerce (the where-is-my-order follow-up) and legal services, without vanity metrics.
REVsign's process documentation is written to be quoted by a model, not just read by a person: exact rules and thresholds, with each page's facts declared in a format machines read and a dedicated file that tells assistants what is published and where. It is the same architecture that lets an AI assistant answer with the correct source instead of making one up.
Traditional crawls fail when they require an exact label. We evaluate in two steps whether a person really holds the function you need and with what intent, even when their job title inside the company is unusual.
We protect budget by validating before spending: the credit that reveals someone's email or phone is used only once the AI has confirmed that person holds the required function. The result is zero spend on false positives.
On every cycle we take a picture of the software each account uses and compare it against the previous one. When a tool comes in or drops out, that difference is weighted by fixed rules and produces an alert ready for the client's CRM, anticipating churn before it shows up in billing.
Before any enrichment, domains are normalized and visited to confirm the site actually responds. Dead or merely parked ones are quarantined, so nothing is paid to analyze domains that do not work.
To capture intent before a prospect evaluates a competitor, the radar does three things: it identifies which company a visitor's connection belongs to, looks that domain up in our corporate relationship graph, and scores the visit with fixed rules. Only if it clears a strict threshold does it route to anyone.
The engine maps holding companies and controlling entities with relationships verified against a real source, and leaves inferred ones as a draft for review instead of treating them as fact. That keeps an opaque account, with its subsidiaries and linked companies, traceable without inventing ties no one confirmed.
To solve the market's chaotic funnels, we force every digital footprint to converge into the Single Signal Layer: companies, people and narratives in one base, with no copy per system. All commercial orchestration and intelligence capture rests on that foundation, with no duplication and no manual reconciliation.
We generate multichannel packages and drafts by applying a digital straitjacket to sub-agent behavior: how much each one may write and the exact shape its answer must have are carved into the code. Any deviation halts execution on the spot, instead of leaving the model spinning as it tries to correct itself.
We solve, in the moment, the friction of spending budget on prospects that already moved to negotiation. The exclusion queue drains instantly, with no two processes stepping on each other, and those records are ready to be pulled from the audiences targeted in Meta Ads or LinkedIn Ads.
We manage the disorder of external data by opposing it with an extremely rigid internal shape. Everything that comes in, websites and third-party systems, passes through a filter that rejects whatever does not match the expected shape, so the AI ecosystem only ever receives structured, action-ready signals.
Every new piece of data in the engine is born with its per-client isolation already in place, not as a later patch. That is what lets multiple accounts run on the same infrastructure without a single record crossing from one to another.
Every automated step can be repeated without duplicating anything, and if an external provider fails it cuts itself off instead of retrying forever. Each agent's consumption is metered separately, so the cost of orchestration stays auditable instead of a billing black box.
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