90%+Business metric
Client Data Search & Enrichment
We define priorities, use cases, architecture, and a practical roadmap aligned with your business goals.
AI Sales Software Solutions
Sales AI that is built into the way your team already sells
Decentralized finance lending protocols let you lend or borrow cryptocurrency through smart contracts instead of banks or brokers. By TVL, the top DeFi lending platforms are Aave, Morpho, Spark, Compound, and Fluid. While some lending platforms combine all funds into one pool, others separate their lending processes into separate markets to isolate the effects of any problem that may occur within an asset.
This year, the most popular protocols will not vary much in their core business model, but they will differ in how they address risks, which blockchains they operate on, and whether they cater to retail customers, institutional clients, or both. In the following article, we will look at the top DeFi lending protocols, how they operate, and takeaways for businesses.
Decentralized finance lending protocols let you lend or borrow cryptocurrency through smart contracts instead of banks or brokers. By TVL, the top DeFi lending platforms are Aave, Morpho, Spark, Compound, and Fluid. While some lending platforms combine all funds into one pool, others separate their lending processes into separate markets to isolate the effects of any problem that may occur within an asset.
This year, the most popular protocols will not vary much in their core business model, but they will differ in how they address risks, which blockchains they operate on, and whether they cater to retail customers, institutional clients, or both. In the following article, we will look at the top DeFi lending protocols, how they operate, and takeaways for businesses.
Our technology
Industry-Specific AI Transformation Solutions
Our AI transformation agency works throughout various industries, but we never treat them as interchangeable; we approach every project by focusing on the real challenges of that specific industry, rather than relying on generic templates.
- OpenAI
- Anthropic
- Google Cloud
- AWS
- Microsoft Azure
- NVIDIA
- Python
- PyTorch
- TensorFlow
- LangChain
- Hugging Face
The Bottleneck
What’s Slowing Down Your Sales Pipeline
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.
Stop losing revenue to a slow, manual funnel
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.
Book a ConsultationWhat We Build
Our AI Sales Solution
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.
About solution
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.
About solution
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.
About solution
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.
About solution
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.
Delivery Model
How It Works
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.
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Discovery
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.
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Pilot
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.
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Implementation
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.
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Support & Expansion
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.
See it running on your own pipeline first
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.
Request a Pilot
Best Fit
Who Benefits from AI Sales Software
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.
B2B and SaaS Companies
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.
- Instant routing of trial and demo requests
- Product usage signals folded into lead scoring
- Automated follow-up between demo and decision
Sales Leaders
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.
- Forecast built on complete, current CRM data
- Objection and win-loss patterns surfaced from real calls
- Coaching grounded in transcripts, not recollection
Enterprise Sales
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.
- Buying-committee mapping across accounts
- Role-based access and full action audit trail
- On-premise or private-cloud deployment available
Founders and Small Sales Teams
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.
- Outbound capacity without additional headcount
- Consistent follow-up while founders are delivering
- Deployment measured in weeks, not quarters
Plans
Choose a plan — we'll set the agent up for your team
$500/mo after launch: Tokens, servers, support and updates as your process changes.
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Start
$2,500up to 5 reps
- Chats and phone
- Connection to your CRM
- Summaries, tasks, stages
- Set up for your pipeline.
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Business
$5,000up to 15 reps
- Everything in «Start»
- Email, calendars, social media
- Multiple pipelines and roles
- Role-based approval rules
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Custom
Quotedno limits
- ERP and accounting systems
- Custom integrations
- Price after a technical audit
Get to Know Us
Our Solutions
Why Choose Braintix as Your AI Transformation Company
Choosing the right partner is even more important than the underlying technology. That’s why companies that are seeing real results from artificial intelligence are choosing Braintix.
Request a Free Call-
Good communication and collaboration
We operate in short cycles and make each process simple to understand. Your team actively engages in AI-driven business transformation, and you remain informed throughout.
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90%+ of what we build stays in production
More than 9 out of 10 of the systems we implement are still up and working a year after launch. You’re paying for AI that keeps going for the long, not for prototypes that quietly get shelved.
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AI built around your business
We work with the unique characteristics that you already possess — your data, procedures, and customers. You receive a finished product that is entirely yours.
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The results you can see in weeks
From the start, we immediately focus on the use cases that generate profit. You rapidly obtain tangible results, without being dragged into a prolonged evaluation phase that exhausts your budget.
Other AI projects we've delivered
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80%+Business metric
Client Data Search & Enrichment
We define priorities, use cases, architecture, and a practical roadmap aligned with your business goals.
20%+Business metric
Client Data Search & Enrichment
We define priorities, use cases, architecture, and a practical roadmap aligned with your business goals.
The main responsibility of an AI transformation consulting firm is to help your company identify where AI can add value and develop and implement the right solutions. Generally, this entails analyzing your data, technology, and processes, then prioritizing use cases by urgency. After that, the company implements, integrates it into its processes, runs pilot tests, and deploys the solution to various teams. The best partner will also perform some less attractive, but crucial, actions, such as data cleaning, cybersecurity, monitoring, and staff training, all necessary for implementation. And at Braintix, we don’t stop working on your project after our presentation — our efforts bear fruit once yours do.
The ROI for your artificial intelligence deployment project is calculated by linking each project to a business metric you have already been monitoring and comparing that metric before and after implementation. Possible indicators include: time saved per task, reduced customer service costs, increased revenue, fewer errors, or shorter turnaround times. The key here is to select the metric before the project launches and to finalize it, rather than discussing it only after the project is already complete.
Most business transformation with AI follow four distinct phases: assessment, roadmap development, implementation, and scale. The assessment phase is about objectively evaluating the data, systems, people, and problems to determine what's really feasible. The development of the strategy entails ranking scenarios based on their value and feasibility and developing a roadmap that encompasses prioritization, costs, and time frame. The development process is where technology is developed, integrated into live systems, and tested on unstructured real-world data, all in a safe, monitored environment. In the scale phase, what is working is extended to other departments and locations, the infrastructure can handle the load, and the initiative is fully turned over to your group.
Yes – Braintix, a company that help with AI transformation strategy, with trust and governance in mind from the very start, unlike after the artificial intelligence was built. Every system comes with capabilities including monitoring, tracking, and protection, so you can see what the AI is up to and take action when needed. In cases where there are regulations governing your activities, whether loaning, medicine, or otherwise, the models will have the capability to explain and verify their decisions for you. You’ll have an AI you can trust and explain to a regulatory agency or an inspector.
Small businesses don't have a multi-tiered approval system, so they can launch a working system in just a few weeks, whereas large companies can take months. It's important to start with small goals, solving one or two key problems, and showing their impact. In this case, there’s no need for a large in-house data science team, since an external partner can kickstart the effort, and the business team will gain the skills needed to maintain the AI going forward. Advances in cloud systems and machine learning models have reduced costs, so you no longer need an enterprise-level budget to implement useful artificial intelligence.
If you want an AI adoption strategy to help your business innovate, you need to understand whether you have a relevant business challenge, whether you have the necessary data, and whether your organization is ready to change its business processes. Key indicators include routine manual work that takes up your employees’ time, decisions based on gut feeling that can be improved with data, or a growing volume of work that can no longer be handled by hiring additional staff. Our process is affordable and efficient, ensuring you don’t invest resources in a potentially unsuccessful project.