Braintix Labs builds AI sales solutions that plug into your existing CRM, telephony and outreach channels instead of replacing them. We start from a measurable revenue problem — leads going cold, reps buried in data entry, pipelines that stall after the first touch — and put an AI layer exactly where that money is leaking.
We are an applied AI company, not a SaaS vendor with one fixed product. Every deployment is scoped against your data, your sales process and your definition of a qualified lead.
Our team has shipped speech-to-text call analysis, retrieval-augmented assistants, predictive lead scoring and CRM automation into live commercial teams. We work daily with HubSpot, Pipedrive, Salesforce, amoCRM and Bitrix24, and we know where each one breaks under automation. That practical grounding is why our systems behave like a disciplined rep rather than a generic chatbot bolted onto your website.
Business problem first, technology second. We map your funnel, find the stage where conversion actually drops, and automate that stage before touching anything else. Nothing is promised on accuracy until we have tested it against your historical deals. Critical actions — a proposal, a discount, a contract — always keep a human in the loop.
No parallel system for your reps to maintain. Enrichment, scoring, sequencing and logging happen inside the tools your team already opens every morning, synced through native APIs and webhooks. If a workflow lives in your CRM today, it still lives there after we deploy — only now it fills itself in.
Most sales teams do not have a lead volume problem. They have a capacity problem: too much of the working day goes to sorting, typing and chasing rather than selling. Here is where that time disappears.
Reps open every inbound form, every list purchase and every event scan by hand, and most of them are never going to buy. Studies of B2B teams consistently put the share of a rep’s week spent on non-selling admin at roughly two thirds. The cost is not only the wasted hours — it is the good lead sitting fourth in the queue while someone works through the noise. Automated ai lead qualification software removes that queue entirely by scoring and routing before a human ever looks.
Adding pipeline traditionally means adding headcount. Each new SDR needs recruiting, ramp time, tooling and management attention, and output stays roughly linear with the number of seats. Meanwhile the volume of research and personalisation buyers expect keeps rising. Teams end up choosing between reaching more accounts and reaching them well.
Response speed is the single most controllable variable in inbound conversion, and it is the one most often lost. A lead that fills in a form at 7pm on Friday and hears nothing until Monday afternoon has already opened three competitor tabs. Weekend, night and lunch-hour gaps quietly delete a measurable slice of your funnel every month.
Deals sit in the wrong stage, call outcomes are never logged, and next steps live in someone’s notebook. When the pipeline data is unreliable, forecasting becomes guesswork and managers coach on anecdotes instead of evidence. Every reporting cycle then starts with a data clean-up rather than a decision.
Book a 30-minute working session. We will map your current pipeline, show where the leaks are, and tell you honestly whether automation is worth it for your setup.
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Our ai sales solutions cover the full commercial cycle: finding the right accounts, deciding which ones deserve attention, reaching them with a relevant message, and keeping the record straight afterwards. Each module below runs independently, so you can start with one and expand once it proves out.
Every inbound and outbound contact is enriched, checked against your ideal customer profile and classified within seconds of arriving. The system pulls company size, industry, tech signals and intent data, compares them to the deals you have actually closed, and assigns a verdict with the reasoning attached. Reps receive a short, ranked list instead of a raw inbox — and a written explanation of why each name is on it. Disqualified contacts are not deleted; they are parked into nurture so nothing of value is lost. Unlike generic ai lead generation software, the scoring logic is trained on your pipeline rather than on an industry average.
Sequences are drafted per account, not per segment. The model reads the prospect’s site, recent news, role and the history you already hold, then writes an opener that references something specific and true. Cadence, channel and follow-up timing adapt to how each contact behaves. This is where ai sales outreach automation stops meaning “the same email to two thousand people” and starts meaning two thousand emails that each deserve a reply. Every message can be routed through human approval until you trust the output.
Calls are transcribed, summarised and written back as structured fields: contact details, budget signals, objections raised, agreed next step and owner. Deal stages move on real evidence rather than someone remembering to drag a card. Because the assistant writes into your CRM through its native API, your existing dashboards, permissions and reports keep working untouched — they simply become accurate. Teams evaluating ai sales assistant software usually find this the hardest part to get right, and it is the part we build first.
A predictive model ranks open opportunities by probability of closing and flags accounts that are drifting toward churn before renewal season. Managers see which deals genuinely need attention this week, and reps stop spreading effort evenly across a list where a fifth of the names carry most of the revenue. The model retrains on your closed-won and closed-lost history, so it gets sharper as your pipeline grows.
We deploy in four stages, and you can stop after any of them. Nothing goes into production before it has been proven on your own data — a full rollout typically runs six to ten weeks from kickoff.
We audit your funnel, CRM hygiene, call recordings and current tooling, then agree on the single process to automate first and the metric that will judge it. Output: a written scope with success criteria, integration map and data requirements.
A limited proof of concept runs on one team, one segment or one channel, against your real historical deals. You see accuracy, coverage and cost per qualified lead before committing further. Typical pilot length: two to four weeks.
The validated workflow is built out properly: CRM integrations, permissions, escalation rules, human approval gates, dashboards and rep training. We roll out team by team so nothing stops while the switch happens.
We monitor model quality, retrain on new outcomes and fix drift as your offer and market change. When the first process is stable, we extend the same ai sales agent into adjacent ones — renewals, upsell, partner channels.
We start with a short pilot on real data. If the numbers do not hold up, you have lost a few weeks — not a year-long platform contract.
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AI sales for companies with a repeatable, multi-touch funnel pays back fastest. If your reps run the same research and follow-up motions week after week, that pattern is exactly what a model learns well.
High lead volume, long consideration cycles and a well-documented ICP make B2B and SaaS the natural first fit. Trial signups, demo requests and content downloads all arrive with enough signal for a model to sort them reliably.
Heads of sales and revenue operations gain a pipeline they can actually forecast from. Because call outcomes and stage changes are logged automatically, weekly reviews start from evidence instead of a data clean-up.
Long enterprise cycles involve many stakeholders and months of silence between touches. Automation keeps every account warm and every contact mapped, with strict permission and audit rules that satisfy security review.
When two people carry the whole commercial function, ai powered sales development gives them the research and follow-up capacity of a much larger team without the hiring risk that comes with it. In practice, ai sdr software covers the prospecting hours a two-person company simply does not have.
Illustrative examples of the kind of AI sales systems Braintix deploys — not client case studies, just a look at the shape of the work.
AI runs the CRM and task list — creates and updates records, balances workload, prioritises deals and sends reminders. No dropped deals, no manual data entry.
A predictive model ranks open leads by probability of closing and flags accounts drifting toward churn, so reps focus where the revenue actually is.
Instant natural-language search across the client base, automatic extra research on each contact, and ready-made offers built from their data.
Yes. We integrate through native APIs and webhooks with HubSpot, Pipedrive, Salesforce, amoCRM and Bitrix24, and we can connect to any CRM that exposes a documented API. Your fields, pipelines and permissions stay as they are — we write into the structure you already use rather than asking you to migrate.
AI sales software is a system that uses machine learning and language models to handle the repeatable parts of selling: researching accounts, qualifying leads, drafting outreach, transcribing calls and updating records. It works as a layer over your existing data — CRM entries, call recordings, website activity and enrichment sources feed the model, business rules decide what happens next, and the result is written back into the tools your team already uses. Humans keep control of everything commercially sensitive.
Yes. Scoring models, message templates, languages and routing rules are configured per team, region or product line while reporting stays consolidated. We usually prove the setup with one team first, then replicate it — each additional rollout is faster because the integration work is already done.
Inbound forms, chat and phone enquiries, event and webinar lists, marketplace and partner referrals, and cold outbound lists. Qualification uses firmographic data, declared need, engagement history and your own closed-deal patterns. Where a lead is genuinely ambiguous, the system says so and escalates rather than guessing — an honest “needs a human” is more useful than a confident wrong score.
Pricing depends on the number of processes automated, integration complexity and volume, so we quote after Discovery rather than from a rate card. A scoped pilot is deliberately small and fixed-price, which lets you judge the return before committing to a full deployment. Ongoing cost splits into support and the underlying model usage, both of which we show transparently.
Tell us where your pipeline is leaking. We will come back with a concrete first step, not a generic proposal.
Ukraine — working with teams worldwide