Commercial discovery
We start with the pipeline and receivables: where quotes stall, follow-up dies, invoices sit, or collections go cold. You leave with 3–5 processes ranked by cash recovered and hours returned.
Short timeline · any system
CRM, chat, invoicing, scheduling, collections, costing, and operations: we connect the model to what you already run, in the short term.

AI in your software
We wire AI into the software you already run.
The model quotes, collects, schedules, and invoices on the systems you already run. That is how AI enters daily operations.
The bridge between AI and daily work — sales, collections, care, operations — on the systems you already pay for. Delivered in weeks.
We start with the pipeline and receivables: where quotes stall, follow-up dies, invoices sit, or collections go cold. You leave with 3–5 processes ranked by cash recovered and hours returned.
If it has an API, webhook, mailbox, or export, it can be wired in the short term. CRM, ERP, helpdesk, storefront, calendar, billing, spreadsheet. The agent reads, writes, and fires actions. A person steps in to sign or when the case is ambiguous.
RAG means retrieval-augmented generation: the assistant searches your price lists, credit policy, discounts, contracts, and collection scripts first. Answers cite the source.
WhatsApp, web, voice, or the desk you already have. The agent quotes, takes orders, books appointments, checks balances, and escalates to a human with context, in your brand voice.
Issue or look up invoices, book visits, estimate costs, move inventory or work orders. AI operates on the system you already have. Master data stays where it lives today.
For salespeople, collectors, and ops leads: account summary, next step, follow-up draft, aging alert. Internal productivity first; the public channel once the rail is trustworthy.
If the company already runs it, it can be wired in. Bring yours.
What RAG means
RAG adds a search step. It opens your price list, collections policy, contract, or account history first, then answers, with a citation.
The question — price, terms, balance, discount, appointment — is searched across your files and systems: commercial policy, ERP, billing, CRM. Only what belongs to that account or product comes in.
Those snippets are attached to the prompt. The model does not “remember” your company. It has this turn’s commercial rule in front of it.
It drafts the follow-up, quote, or collection reminder in your brand voice, and points to the clause or the document. If there is no evidence, it does not close the deal blind.

“The usual discount is 15% at 30 days.” It sounds right, but it does not match your price list and finance will not sign it.
“Price list v.6, tier-A customers: 8% from $4,000, net 15. Account current per the ERP.” There is a source. There is a log. You can collect on it.
It pays off when sales and collections ask the same thing all day and the answer lives in a PDF, an ERP, or a credit policy.
We start with sales and collections because that is where cash shows up. The same pattern applies to any flow that still copy-pastes between systems.
The agent builds the proposal with live prices and stock, drops it in the CRM, and flags the salesperson if margin falls outside policy.
Reminders, payment matching, aging priority, a script for the collector. The human negotiates. AI does not let the invoice go cold in an inbox.
Order, balance, complaint, “when does it arrive?”. It queries the system and escalates with the full thread.
Issue, look up, correct, or push into the billing tool you already have. AI operates on that system.
Appointments, site visits, installs, demos. It crosses calendars, routes, and availability, then confirms on the channel the customer already uses.
Estimates, work orders, inventory, shop-floor reports. Any operational process with a system behind it is a candidate. If there is an API or a file, there is a rail.

OpenAI, Claude, Gemini, Azure, Bedrock, Vertex. We pick for cost, latency, data residency, and Spanish quality. The value is wiring your operation.
We pick one flow with real volume — usually sales or collections. We measure cycle time, leakage, and where a person must sign.
One agent in the CRM, chat, or billing tool you already use. Logs, costs in view, rollback. Week by week until the team prefers it to the spreadsheet.
Same recipe: care, scheduling, costing, the floor. Training and cost per transaction. The code and the commercial rules belong to the company.
Useful AI leaves a trail: which price it quoted, against which list, who approved the discount.
We wire the model to the company’s systems — the ones you already have, or the ones we need to build — and we measure sales cycle, days sales outstanding, and cost per conversation.

No. We connect the model to the CRM, collections, chat, invoicing, or scheduling you already run, if there is an API, webhook, mailbox, or export.
Retrieval-augmented generation: the assistant searches your price lists, policies, and contracts first, then answers with a citation. The walkthrough is higher on this page.
In the short term, on one real-volume flow — usually sales or collections — on software you already pay for. Weeks, not a year-long programme.
By the flow to connect, not by “adding a chatbot.” Quote or WhatsApp.
Any system can be connected. On a call we pick the first one.