Munzer Dwedari

Traditional Retrieval-Augmented Generation (RAG) made Large Language Models (LLMs) useful for enterprise knowledge by giving them access to external documents at answer time. The usual loop is simple: encode the user question in an embedding vector, perform semantic search by retrieving the top matching chunks from a vector database, place those chunks in the prompt, and ask the model to answer. This works well when the answer lives in one obvious paragraph whose wording is close to the user&#39;s wording. However, it breaks down when the answer requires navigation across multiple layers of the knowledge base.
That limitation shows up often in technical sales and applications engineering in the semiconductor industry. A customer rarely asks in the exact language used by a datasheet, and the answer is often not on the first page that mentions the part number. It may live in a linked PDF, a product family specification, a technical appendix, an environmental compliance page, or a table that applies only after the part is classified correctly. Traditional RAG treats these as independent chunks. It does not naturally ask, &quot;What document types did I land on?&quot;, &quot;What document does this page link to?&quot;, or &quot;Should I search inside that exact document next?&quot;
Agentic RAG changes the retrieval workflow from a one-shot lookup into a controlled investigation. The recent survey on <a href="https://arxiv.org/abs/2501.09136">Agentic Retrieval-Augmented Generation</a> describes this shift as adding planning, tool use, reflection, and adaptive control to RAG systems. Instead of retrieving once, the model can decide what to search, inspect intermediate evidence, narrow scope, and search again. Let&#39;s look at an example question based on TDK&#39;s public website to make the difference concrete. The question is: &quot;Does RSEN-2030 contain PCBs?&quot; A conventional vector search can find the RSEN-2030 product page, but the answer is not simply on that page. The useful evidence is on TDK-Lambda&#39;s environmental page about asbestos, PCBs, and mercury. The new agentic search flow first finds the RSEN-2030 page, then follows the environmental reference, then searches that exact URL. The final answer correctly states that RSEN-2030 is a production product and that production products in the latest catalog are listed as &quot;Non-contained&quot; for PCBs, with references to both the <a href="https://product.tdk.com/en/search/emc/emc/power-line/info?part_no=RSEN-2030">product page</a> and the <a href="https://www.jp.lambda.tdk.com/en/environment/asbestos_pcbs_and_mercury.html">environmental page</a>.
<h2>Our Approach to Agentic RAG</h2>
Knowledge Structure at ept AI
At ept AI, we manage knowledge as configured, governed sources that can be assembled for different channels, teams, and customers. A <a href="https://docs.ept.ai/docs/configuration/knowledge-sources/">Knowledge Source</a> can be a website, PDF library, community forum, GitHub repository, documentation, Zendesk or HubSpot knowledge base, or anonymized support tickets. A <a href="https://docs.ept.ai/docs/configuration/knowledge-source-configurations/">Knowledge Source Configuration</a> combines those sources into the exact body of knowledge a channel is allowed to use.
Our Agentic RAG Tool
<img src="/images/blog/agentic-retrieval/fig1.svg" alt="Fig. 1: AI workflow using Traditional RAG vs Agentic RAG. Unlike Traditional RAG, in Agentic RAG the LLM has the ability to do multiple loops of knowledge retrieval where it decides what the query is, and how the search space is constrained.">
Fig. 1: AI workflow using Traditional RAG vs Agentic RAG. Unlike Traditional RAG, in Agentic RAG the LLM has the ability to do multiple loops of knowledge retrieval where it decides what the query is, and how the search space is constrained.
Our agentic knowledge retrieval tool for LLMs is built around that knowledge base model. The tool description tells the LLM which Knowledge Sources exist in the active Knowledge Source Configuration, including each source name, ID, type, description, and source URL. The LLM does not search an anonymous pile of chunks. It chooses which Knowledge Source to search and what to search (see Fig. 1). This capability allows users to guide LLMs how to look for information for a given task like customer support, e.g.: &quot;first look in historical tickets, then in private documentation and finally the website before getting back to the user.&quot; Another benefit of this approach is that the LLM is in control of what queries get used for the search and can do repeated searches to optimize the search query. This turns many &quot;I don&#39;t have enough information&quot; answers to actual answers grounded in data. Yet, even within a Knowledge Source, the information can be too scattered and result in inaccuracies with vector search.
<img src="/images/blog/agentic-retrieval/fig2.svg" alt="Fig. 2: Query search constrained to two subsets of data of two Knowledge Sources. The colored overlayed boxes represent tags applied to a set of documents. One document can have many tags (overlapping colored boxes). The dotted boxes are the constrained search space chosen by the LLM.">
Fig. 2: Query search constrained to two subsets of data of two Knowledge Sources. The colored overlayed boxes represent tags applied to a set of documents. One document can have many tags (overlapping colored boxes). The dotted boxes are the constrained search space chosen by the LLM.
This shift to agentic knowledge retrieval is <a href="https://developer.nvidia.com/blog/traditional-rag-vs-agentic-rag-why-ai-agents-need-dynamic-knowledge-to-get-smarter/">not a new phenomenon</a>. With the introduction of GPT-5, tool use has become much more reliable with LLMs (see <a href="https://openai.com/index/introducing-gpt-5-5/">GPT-5 performance on Toolathon</a>). What we found at ept AI is that for our customers, often large companies with a large portfolio of technical products, e.g. semiconductors, require a more granular search into their data. In addition to Knowledge Source selection, we added the capability for our system to tag data with industry-and-company-specific taxonomy and allow the LLM to constrain its search space to any subset of the tagged data (see Fig. 2). This matters most in industries where the source type changes the authority of the answer. In semiconductors and electronics, a casual webpage, a marketing catalog, a selector guide, a compliance page, and a datasheet do not carry the same weight. For engineering decisions, the answer often needs to be grounded in official datasheets, family specifications, or technical information PDFs. Those documents are the sources teams treat as binding for part selection, operating limits, storage conditions, regulatory claims, and more. Agentic RAG lets us narrow toward those industry-specific, source-specific records instead of relying on whichever chunk happens to be semantically closest.
Consider a troubleshooting question about <a href="https://synaptics-astra.github.io/doc/v/latest/">Synaptics Astra documentation</a>: &quot;Playback audio doesn&#39;t match the behavior defined in the reference voice-assistant example.&quot; In this case, we have instructions in the system prompt to always search the &quot;latest version of documentation&quot; for that specific Knowledge Source. The LLM, having the full visibility of Knowledge Source tags, does not spread across mixed documentation branches or generic product pages. Instead, it stays entirely inside one Astra documentation version, <code>1.6.1</code> (the latest version at the time). The retrieved evidence includes the Astra Linux docs at <a href="https://synaptics-astra.github.io/doc/v/1.6.1/linux/index.html">synaptics-astra.github.io/doc/v/1.6.1/linux/index.html</a> and all retrieved documentation URLs for this response were from the same <code>1.6.1</code> branch, which is exactly the behavior we want for version-sensitive technical questions: once the version context is present, the search stays pinned to one documentation lineage instead of blending evidence across releases.
Putting the user in control is equally important. Users are now able to express retrieval intent in normal language: &quot;search the public website,&quot; &quot;look inside this datasheet,&quot; &quot;use the product page first,&quot; or &quot;only use official docs&quot;. Under the hood, that maps to Knowledge Source Configurations, Knowledge Source IDs, queries, and tags. This gives the user a practical navigation layer over complex knowledge sources. We have already seen customers use it internally to perform documentation discovery and summary to improve their knowledge base.
<h2>Performance Comparison</h2>
We test the new Agentic RAG method in two ways: quantitatively on a set of 100 single-fact questions, and qualitatively, by inspecting traces on selected questions where the answer requires navigating across product pages, documentation versions, and linked PDFs. The point of the evaluation is not simply, &quot;did the model answer?&quot; It is, &quot;did the model find the right evidence path, stay inside the right knowledge boundary, and give the user a source they can trust?&quot;
<img src="/images/blog/agentic-retrieval/fig3.svg" alt="Fig. 3: Quantitative Results. Panel A: bar chart comparing faithfulness for Traditional vs. Agentic RAG, 80% to 85%. Panel B: bar chart comparing average high-relevance retrievals per response, 4.37 to 6.">
Fig. 3: Quantitative Results. Panel A: bar chart comparing faithfulness for Traditional vs. Agentic RAG, 80% to 85%. Panel B: bar chart comparing average high-relevance retrievals per response, 4.37 to 6.
Our first test (Fig. 3) compares the new method to the old one based on 100 single-fact short technical questions (based on real production traffic). For the previous method, these questions are not particularly challenging and are mostly answered correctly. The goal of the test is to verify that the new method matches the correctness of the old method since now we have an LLM driving decisions for the knowledge retrieval. Indeed, we see that the new method produces answers that are similar to the old answer in 96% of the time. In addition, we also see that new LLM response&#39;s <a href="https://arxiv.org/abs/2505.04847">faithfulness</a> improves from 80% to 85%, meaning the LLM&#39;s answer is more grounded in data than the previous method. The previous method returns, on average, 4.37 retrievals with a high relevance score to the question whereas the new method returns 6 high relevant retrievals per response. Relevance here is measured using an <a href="https://arxiv.org/abs/2411.15594">LLM-as-a-judge.</a> Across the same 100 questions, traditional RAG returns 1,534 total retrievals, or almost 16 per response, while the new workflow returns 3,160. That is more retrieval work, but it is not just noise: the number of highly relevant retrievals increases. For technical support and applications engineering, that matters because the final answer is only useful if it is backed by enough precise evidence to be reviewed, cited, or escalated. In practice, we care less about the raw number of retrieved chunks and more about the shape of the evidence. A single loosely related chunk can produce a fluent but fragile answer. A larger set of high-relevance retrievals gives the model a better chance to compare sources, catch missing constraints, and cite the right document. The observed result is exactly the behavior we want: more focused evidence, higher faithfulness, and preserved answer correctness.
For the qualitative evaluation, we inspect 3 different questions on public websites where we have previously seen that the previous method fail. Consider a question about whether <a href="https://product.tdk.com/en/search/emc/emc/power-line/info?part_no=B84143A0008R105"><code>B84143A0008R105</code></a> (a TDK Power Line EMC Filters) can be used at 3000m altitude. A plain semantic match can find the part number page and the datasheet, but the governing detail lives in technical information about altitude derating. The agent searches broadly, finds the relevant datasheet family and technical-information PDF, then narrows retrieval to those documents. The final answer does not stop at &quot;usable&quot; or &quot;not usable.&quot; It explains that standard conditions apply up to 2000m and that 3000m requires derating, giving the current derating factor of 0.925 and voltage derating factor of 0.880. That changes the response from a lookup into an engineering answer. The previous method found the part page, but it does not navigate to the technical-information PDF where altitude derating is defined, so it misses the 2000m baseline and the required 3000m derating factors.
Another example involves storage conditions for <a href="https://product.tdk.com/en/search/sensor/ntc/chip-ntc-thermistor/info?part_no=NTCG103JF103FT1"><code>NTCG103JF103FT1</code></a>, a Chip NTC Thermistor (Sensor). The product page itself is not enough. The useful information is in the NTCG series catalog linked from that product page. With the new method, the LLM first retrieves the product page, then constrains the search to the <a href="https://product.tdk.com/system/files/dam/doc/product/sensor/ntc/chip-ntc-thermistor/catalog/tpd_commercial_ntc-thermistor_ntcg_en.pdf">catalog PDF</a>, and answers with the storage temperature, humidity, and handling notes from that document. This is a common pattern in component documentation: the SKU page identifies the part, but the binding storage, handling, or reliability rule is often in the family-level catalog. The previous method only reaches the product page.
Version-sensitive documentation is another place where a constrained search space matters. In a Synaptics Astra question explicitly framed around ASTRA v1.5.0 and third-party software license information, the retrieved Astra documentation stays on the <code>1.5.0</code> branch, including the <a href="https://synaptics-astra.github.io/doc/v/1.5.0/EULA.html">EULA</a>, v1.5.0 release notes, and a software update user guide. The retrieval set also includes a small amount of non-versioned support material, but it does not mix Astra docs from <code>latest</code>, <code>main</code>, or other versions. The search space constraint is enabled by tagging the data and giving the LLM the visibility and ability to use these tags: the model recognizes that version is part of the retrieval intent and avoids blending documentation across releases, which otherwise would cause the model to hallucinate and answer based on outdated documentation.
<h2>Challenges and Limitations</h2>
While Agentic RAG as described above results in higher accuracy, less hallucination, and better knowledge navigation, it comes with a few challenges. One of the main challenges is prompting: guiding the LLM on how to use the new tool(s). If the prompt is too permissive, the agent may over-search, spend too many tool calls, and return slowly. If the prompt is too restrictive, it falls back into one-shot Traditional RAG behavior and misses linked evidence. The best balance is to set clear goals for the LLM and make every tool orthogonal and explicit: one tool should have a clear job, clear inputs, and clear guidance for when to use it. While our agentic knowledge retrieval tool offers a sophisticated way for LLMs to navigate technical knowledge, the orthogonality question becomes especially challenging when multiple search tools are available, such as internet search or product catalog lookup. Accurate and clear tool descriptions help the LLM avoid confusion about what each tool does and when to use it.
Traceability is the second challenge. Agentic systems are harder to evaluate because the final answer is only part of the behavior. We also need to inspect the path: which tools were called, which URLs were discovered, which tag filters were applied, and whether the answer comes from the right authority. Our in-house traces already record tool calls, queries, retrieved URLs, outputs, and more. That makes it possible to let us tune prompts without guessing and also allows us to inspect latency and improve upon the speed of the agent.
<h2>Conclusion</h2>
Agentic retrieval represents the third phase of applying LLMs to question answering. The first phase was Traditional RAG, in which a single LLM call is applied to retrieved knowledge chunks. The second phase was strong orchestration, where multiple LLM calls are deployed, but in a highly structured workflow. Today, two factors enable the Agentic RAG breakthrough. First, LLMs and their associated prompting methods have become reliable enough to trust the LLM to make its own decisions about what tools to call, what knowledge to pursue, and when it is ready to compose a final answer. Second, the associated infrastructure of knowledge organization, retrieval methods, and performance management have evolved to provide LLMs with effective tools and methodologies. These become especially important in specialized knowledge domains like engineering, sales, and support of technically sophisticated products. This post illuminates some of the methods we have found to be effective at ept AI.

How agentic RAG turns one-shot vector search into controlled, multi-step knowledge discovery—and why that matters for semiconductor and technical product teams.

What Agentic Retrieval Means for Technical Product Knowledge

Amit Rele

Your sales team is covering a lot of accounts. There&#39;s no way to give all of them the time it takes to really understand them — the stakeholders, the competitive threats, which product lines fit the customer&#39;s architecture, what&#39;s happened in every prior conversation. Some accounts get that attention. Others have enough history that the team assumes it&#39;s not needed. Most get whatever the rep can piece together beforehand. It&#39;s a capacity problem.
CRMs capture what happened. They don&#39;t tell you what matters next. They log the contacts you already know — not the stakeholders you&#39;re missing. And they don&#39;t understand semiconductors or hardware — they can&#39;t reason about product fit, application architectures, or which business unit to lead with in a given account. That&#39;s not a knock on CRM. It&#39;s what CRM was built to do.
Generic AI tools don&#39;t solve it either. They know nothing about your products, your deals, or your customers. A Copilot or ChatGPT license can make someone more productive in a general sense. It can&#39;t tell a rep which product line to lead with in a specific account, surface a cross-sell opportunity across business units, or generate a pre-call plan grounded in the actual history of that relationship.
Deal Room is built for that specific problem — and it works with your CRM, not around it.
<h2>What It Does</h2>
Deal Room is a persistent workspace organized around a single deal — its own stakeholder map, its own intelligence thread, pulling from CRM records, email threads, and meeting transcripts, updated continuously as new information comes in. It stays live and is shared across everyone working the deal.
Before every meeting, it generates a pre-call plan in minutes: named stakeholder hot buttons, competitive risks specific to this deal, product angles grounded in what&#39;s actually happened so far. One sales leader described the ask directly — pre-call planning summaries generated automatically, saving significant prep time. That&#39;s not a feature request. That&#39;s a real cost being measured.
Because it understands hardware, it can do things CRM never could. Pre-built battlecards comparing your product vs the competition. Application architectures that map where your products fit. Cross-sell signals that surface not just the lines the rep knows, but adjacent lines they might not pitch — a recently acquired product family, perhaps. In one case, a team used it to identify three relevant product lines for an account that had gone quiet. The customer responded to all three. That email became a team template.
When a rep leaves, the Deal Room stays whole. Every insight accumulated over the life of the deal is there for whoever picks it up next.
<h2>Who It&#39;s For</h2>
Deal Room is built for semiconductor and hardware sales cycles — long timelines, multiple stakeholders, deep product portfolios, design-win-centric pipelines. The buyers are VP Sales, Head of Sales, and CRO — the people accountable for revenue per headcount. The ept AI platform addresses technical depth and market intelligence through other products. Deal Room is the account intelligence layer for the reps running deals.
The underlying problem is structural. Most deal intelligence lives in the heads of the people who built the relationships. What changes when that intelligence becomes a shared, current workspace — accessible to every rep, every time — is not just one deal. It&#39;s what your whole team can carry into every meeting.
We build your demo from your own product catalog and accounts — so you see exactly how Deal Room would work for your team before you commit to anything. Usually 30 minutes. <a href="https://go.ept.ai/meetings/eptai-demo/ept-ai-demo">Request a demo</a>.

Deal Room is a persistent, shared workspace that turns CRM records, emails, and transcripts into account intelligence—pre-call plans, battlecards, and cross-sell signals grounded in hardware.

Deal Room: Account Intelligence for the Entire Sales Team

Your products fit more markets than you&#39;re currently selling into. The challenge isn&#39;t product fit — it&#39;s that understanding where the fit is, who the buyers are, and whether a vertical is worth pursuing requires research that&#39;s slow, time-consuming, with external dependencies and almost always out of date by the time a decision gets made.
Market size estimates live in expensive analyst reports. Competitive landscape mapping means hours of web searches and reading conference programs. The ecosystem of customers, partners, standards bodies, and competitors is scattered across trade publications, LinkedIn, and the heads of the three people on the team who&#39;ve worked that space and in your desired GEO before.
The result is a static snapshot that gets distributed across the company in PowerPoint. Roadmap and GTM decisions made on incomplete information, with months of latency between the market signal and the response.
Some teams try to close this gap with generic AI tools — asking ChatGPT to summarize a market or build a competitor list. That gets you part of the way because it doesn&#39;t connect the market picture with your specific product portfolio. It doesn&#39;t internalize your catalog, your existing customers, or where your products have historically won. The output is generic because the input is generic.
Application Room is different because it&#39;s built on your product knowledge. It understands your catalog — your datasheets, application notes, technical requirements — and uses that context to map an application area in a way that&#39;s specific to your company&#39;s position in it, not a generic overview any competitor could pull. We do that work for you, and we can do it for multiple segments concurrently so your team isn&#39;t starting from scratch every time a new market comes into scope.
<h2>Who It&#39;s For</h2>
Application Room serves the commercial team members responsible for identifying and entering markets — before individual deals are being worked.
Product Managers, Product &amp; Applications Marketing and Business Development who own market analysis and entry decisions and are responsible for translating market intelligence into roadmap direction and field-usable positioning.
Sales teams — use it to ground outreach in the customer&#39;s actual technical environment and to walk into industry events prepared. Rather than generic messaging, they enter conversations knowing the companies in the space, what&#39;s driving their buying decisions, and where the natural product fit is.
<h2>What It Does</h2>
Application Room is a market and ecosystem intelligence workspace. A user describes a target application, vertical, or use case and Application Room assembles a structured picture of that space:
<ul>
<li>Market trends, sizing and segmentation — addressable market, growth vectors, dominant application areas.</li>
<li>Ecosystem mapping — Tier 1 OEMs, emerging challengers, systems integrators, standards bodies, and the relationships between them</li>
<li>Competitive landscape — how competitors are positioned relative to your portfolio, where you have natural advantages, where the gaps are</li>
<li>Technical requirements — what buyers in this market actually need: performance specs, certifications, form factors, integration dependencies.</li>
</ul>
In yesterday&#39;s world, an analyst report from six months ago might have been adequate. In today&#39;s accelerated global market, it won&#39;t reflect what&#39;s happening right now — or in your specific geography. Application Room stays current as the competitive landscape and ecosystem evolve — so when an expansion conversation comes up, it&#39;s grounded in what the market looks like today, not last quarter.
From within an Application Room, a user can identify a target company and generate a deal workspace in a single action — carrying the application context directly into the account pursuit. Because Application Room understands the application landscape, it can also surface adjacent markets and use cases where your portfolio has a natural fit you hadn&#39;t considered — expanding the opportunity picture beyond the vertical you started with.
<h2>Where It Fits in the Sales Motion</h2>
Application Room operates at the top of the funnel — but not in the sense of marketing lead generation. It&#39;s the intelligence layer that determines which markets and accounts should enter the funnel at all.
Before the funnel: A team is evaluating whether to pursue a vertical or application area. Application Room is how they make that decision with data rather than intuition — mapping the opportunity, identifying the players, and surfacing the technical fit before any development or selling effort is committed. This is where the generic AI gap is most acute: a tool that doesn&#39;t know your products can tell you the market exists, but it can&#39;t tell you whether you have a credible position in it.
At funnel entry: Once the decision to pursue is made, Application Room feeds account selection. Rather than building from a generic company list, the team identifies prospects who fit the specific ecosystem picture the Application Room has assembled — with the right product lines and entry angles already mapped.
Deal initiation: From within an Application Room, a user can identify a specific prospect and generate a deal workspace in a single action, carrying the application context directly into the account pursuit. The rep doesn&#39;t start from scratch — they start with the market intelligence already in place.
We build your demo from your own product catalog and target application — so you see exactly how Application Room would work for your team before you commit to anything. Usually 30 minutes. <a href="https://go.ept.ai/meetings/eptai-demo/ept-ai-demo">Request a demo</a>.

Application Room maps markets using your product catalog—trends, ecosystem players, and competitive fit—so technical sales and BD teams pursue the right verticals.

Application Room: Market Intelligence for Technical Sales Teams

Rob Conant

<h2>Some things don&#39;t sell on a datasheet alone</h2>
It&#39;a exciting what AI can do for sales, but it will be put into operation differently in different industries where we have different sales processes. My focus is on enterprise B2B sales, and in my experience in Internet of Things (IoT), smart grid / energy, enterprise software, and semiconductor sales, I&#39;ve seen a common thread in these enterprise B2B sales processes: they&#39;re built around trust and alignment.
Trust and alignment are critical in enterprise B2B sales because your customers are not just buying your product. They&#39;re buying you, your company, your product, and your future products. A formal framework for this comes from economists Phillip Nelson and, later, Michael Darby and Edi Karni. They sorted goods into three categories:
<ul>
<li>Search Goods. The customer can establish the value of through specs. A gallon of gas, or a clearly specified semiconductor product.</li>
<li>Experience Goods. The customer establishes the value only by experiencing your product. A meal at a restaurant, or a microcontroller software stack.</li>
<li>Credence Goods. The customer may not even be able to establish the value after consuming it. Like a medical procedure, a next-generation chip, an investment banker, or an enterprise software platform.</li>
</ul>
<img src="/images/blog/trust-curve-b2b-enterprise-sales.png" alt="Chart showing that in B2B enterprise sales, trust required to close rises sharply as customers move from buying a spec to buying the whole partnership">
<h2>It&#39;s the purchase, not just the product</h2>
Many enterprise B2B products fall into the Credence Good category: you&#39;re not buying a finished thing off a shelf. You&#39;re choosing a partner whose success has to stay intertwined with yours for the value to be realized.
The banker&#39;s advice only pays off if they keep working your interest through a multi-year process. The software platform only delivers if the vendor keeps investing in your use case and your roadmap. The custom subsystem only works if the supplier keeps engineering alongside you through revisions no one could fully spec up front.
That&#39;s why these decisions run on trust. Trust is a forecast about alignment — made before you have any evidence.
However much research the customer does, however much our salespeople help with the evaluation, however many executive meetings align the direction — when the customer finally chooses a supplier, they are taking a leap of faith. We - as salespeople - have to earn it.
<h2>An example: choosing a motion-control subsystem</h2>
A large surgical robotics company is choosing a new motion-control subsystem for its next-generation system. The company sells thousands of these multi-million-dollar pieces of equipment per year, and the reliability, accuracy, and precision of the mechanical subsystem are critical to how the robot performs.
The subsystem has to be custom designed for this next-generation system, so there is no historical data for this new motion-control subsystem — nothing to benchmark against, no installed base to point to.
The company is left to evaluate not just the technical reliability of the motion-control subsystem, but also the business integrity and reliability of the supplier itself, because it&#39;s committing to years of shared engineering, not a one-time delivery. The decision comes down to the credibility and track record of the supplier as much as the expected performance of the product.
<h2>Earning trust</h2>
That credibility and performance of the supplier is not something that shows up on a datasheet. It is something that is earned, every day, by great salespeople.
AI can help with this, but cannot replace it.
That is what we keep in mind every day as we serve salespeople at ept AI.

Trust - critical for enterprise B2B sales - is something that is earned, every day, by great salespeople.

All articles

What Agentic Retrieval Means for Technical Product Knowledge

Deal Room: Account Intelligence for the Entire Sales Team

Application Room: Market Intelligence for Technical Sales Teams

When Your Product Can't Be Bought on Spec