Every business generates large amounts of information that could improve decision making. The challenge is rarely collecting that information. It is turning scattered, unstructured data into something people can trust and act on consistently.
Digiteum built DealPulse to solve that challenge in sales.
After years of selling complex software projects, we realized our own pipeline reviews depended on the same things many businesses rely on: CRM updates, meeting notes and personal judgment. Every customer conversation added new information, yet nobody had time to revisit hours of recordings before deciding which opportunities deserved another week of effort. Deals moved forward because activities had been completed, not always because customers had demonstrated genuine buying intent.
We decided to automate that process.
Every business generates large amounts of information that could improve decision making. The challenge is rarely collecting that information. It is turning scattered, unstructured data into something people can trust and act on consistently.
Digiteum built DealPulse to solve that challenge in sales.
After years of selling complex software projects, we realized our own pipeline reviews depended on the same things many businesses rely on: CRM updates, meeting notes and personal judgment. Every customer conversation added new information, yet nobody had time to revisit hours of recordings before deciding which opportunities deserved another week of effort. Deals moved forward because activities had been completed, not always because customers had demonstrated genuine buying intent.
We decided to automate that process.
The objective was to automate the collection, validation and organization of evidence hidden inside recorded conversations, giving decision makers a reliable picture of every opportunity before investing more time and resources.
The information already existed inside the transcripts. The difficulty was extracting only what had actually been confirmed by the customer, separating facts from assumptions, preserving that information across multiple conversations and presenting it in a way that supported consistent business decisions. The platform had to identify whether a customer had confirmed a business problem, demonstrated buying authority, described operational impact or agreed meaningful next steps. Every conclusion needed to be traceable back to the conversation itself. If the transcript could not support a statement, the platform could not report it.
The objective was to automate the collection, validation and organization of evidence hidden inside recorded conversations, giving decision makers a reliable picture of every opportunity before investing more time and resources.
The information already existed inside the transcripts. The difficulty was extracting only what had actually been confirmed by the customer, separating facts from assumptions, preserving that information across multiple conversations and presenting it in a way that supported consistent business decisions. The platform had to identify whether a customer had confirmed a business problem, demonstrated buying authority, described operational impact or agreed meaningful next steps. Every conclusion needed to be traceable back to the conversation itself. If the transcript could not support a statement, the platform could not report it.
To achieve that, we deliberately avoided asking a single AI model to analyze an entire conversation. Instead, we decomposed the business process into a sequence of specialized operations.
One AI agent extracts factual information from the transcript.
Another validates those findings and removes anything that cannot be directly supported by the conversation.
A third maintains the accumulated state of the opportunity across every customer interaction, allowing the platform to understand how the relationship develops over time instead of treating every meeting in isolation.
A reporting agent then produces structured summaries of both the latest call and the overall deal.
To achieve that, we deliberately avoided asking a single AI model to analyze an entire conversation. Instead, we decomposed the business process into a sequence of specialized operations.
One AI agent extracts factual information from the transcript.
Another validates those findings and removes anything that cannot be directly supported by the conversation.
A third maintains the accumulated state of the opportunity across every customer interaction, allowing the platform to understand how the relationship develops over time instead of treating every meeting in isolation.
A reporting agent then produces structured summaries of both the latest call and the overall deal.
One architectural decision shaped the entire platform. AI never produces the commercial recommendation.
Large language models are extremely effective at understanding language and extracting evidence from conversations. They are far less suitable for making business decisions that must always follow the same rules.
Once the evidence has been collected and validated, a deterministic decision engine evaluates it using conventional software and returns one of three recommendations: keep investing, pause, or stop investing. Separating evidence extraction from business logic makes every recommendation reproducible, testable and fully explainable.
One architectural decision shaped the entire platform. AI never produces the commercial recommendation.
Large language models are extremely effective at understanding language and extracting evidence from conversations. They are far less suitable for making business decisions that must always follow the same rules.
Once the evidence has been collected and validated, a deterministic decision engine evaluates it using conventional software and returns one of three recommendations: keep investing, pause, or stop investing. Separating evidence extraction from business logic makes every recommendation reproducible, testable and fully explainable.
Working with real customer conversations quickly demonstrated why this separation matters.
Early in development, one analysis interpreted a signed NDA as commercial commitment and moved the opportunity to a late sales stage. For an experienced salesperson the mistake was obvious. An NDA simply allows discussions to continue.
For software, however, it exposed a missing business rule. We corrected the behavior at two levels. The deterministic engine now prevents contractual progress unless earlier commercial milestones have genuinely been reached, while the extraction prompts were refined to exclude preliminary paperwork, hypothetical discussions and indicative pricing. Because prompts are versioned independently from application code, the analytical behavior could be updated immediately without redeploying the production platform.
Similar edge cases appeared throughout development and gradually strengthened both the AI pipeline and the deterministic rules that sit behind it.
Working with real customer conversations quickly demonstrated why this separation matters.
Early in development, one analysis interpreted a signed NDA as commercial commitment and moved the opportunity to a late sales stage. For an experienced salesperson the mistake was obvious. An NDA simply allows discussions to continue.
For software, however, it exposed a missing business rule. We corrected the behavior at two levels. The deterministic engine now prevents contractual progress unless earlier commercial milestones have genuinely been reached, while the extraction prompts were refined to exclude preliminary paperwork, hypothetical discussions and indicative pricing. Because prompts are versioned independently from application code, the analytical behavior could be updated immediately without redeploying the production platform.
Similar edge cases appeared throughout development and gradually strengthened both the AI pipeline and the deterministic rules that sit behind it.
Building DealPulse meant solving much more than transcript analysis. We developed the operational foundation required for a commercial SaaS product, including secure authentication, administration, audit history, usage metering, account lifecycle management, GDPR compliant data handling, documented privacy and security controls, and automated data retention and deletion policies.
By the time the AI pipeline was complete, the platform was ready to support real users, real customer data and day to day business operations.
Building DealPulse meant solving much more than transcript analysis. We developed the operational foundation required for a commercial SaaS product, including secure authentication, administration, audit history, usage metering, account lifecycle management, GDPR compliant data handling, documented privacy and security controls, and automated data retention and deletion policies.
By the time the AI pipeline was complete, the platform was ready to support real users, real customer data and day to day business operations.
Before releasing DealPulse, we validated the complete production workflow using real sales conversations. Every recommendation is traceable to supporting evidence in the transcript, allowing analytical decisions to be reviewed, challenged and continuously refined as more real world outcomes become available.
Before releasing DealPulse, we validated the complete production workflow using real sales conversations. Every recommendation is traceable to supporting evidence in the transcript, allowing analytical decisions to be reviewed, challenged and continuously refined as more real world outcomes become available.
DealPulse has already demonstrated the wider value of this engineering approach. The platform transforms unstructured conversations into structured operational data, automates repetitive evidence gathering and applies business rules consistently while leaving final accountability with people. That combination allows organizations to automate complex knowledge work without surrendering business judgment to AI.
Although the first implementation focuses on sales, the underlying architecture is applicable wherever organizations make operational decisions from large volumes of unstructured information. Customer service, claims processing, compliance, procurement and case management all follow the same pattern: collect evidence, validate it, preserve context across multiple interactions and apply consistent business rules before presenting the result to a human decision maker.
DealPulse has already demonstrated the wider value of this engineering approach. The platform transforms unstructured conversations into structured operational data, automates repetitive evidence gathering and applies business rules consistently while leaving final accountability with people. That combination allows organizations to automate complex knowledge work without surrendering business judgment to AI.
Although the first implementation focuses on sales, the underlying architecture is applicable wherever organizations make operational decisions from large volumes of unstructured information. Customer service, claims processing, compliance, procurement and case management all follow the same pattern: collect evidence, validate it, preserve context across multiple interactions and apply consistent business rules before presenting the result to a human decision maker.
DealPulse is currently expanding from individual deal analysis toward organization wide pipeline reporting and operational intelligence.
The project also serves as a production example of Digiteum’s approach to AI engineering: analyzing a business process, identifying where automation creates measurable value, combining AI with deterministic software where each is most effective, and delivering production systems that people can trust.
DealPulse is currently expanding from individual deal analysis toward organization wide pipeline reporting and operational intelligence.
The project also serves as a production example of Digiteum’s approach to AI engineering: analyzing a business process, identifying where automation creates measurable value, combining AI with deterministic software where each is most effective, and delivering production systems that people can trust.
