Deepline Deepdives.

Jai frames the session around three asks: wealth-management prospecting, updating CRM fields from call transcripts, and improving B2B ad audience match rates.

The wealth-management demo combines public and private data sources, including FAA records, LLC ownership, addresses, and enrichment providers.

The workflow moves from one-time list building to persistent datasets that can be refreshed through a CRM, customer database, schedule, or signal radar.

Jai shows a workflow for extracting structured CRM information from Fireflies call transcripts and writing fields back into HubSpot.

The ads audience section explains how enriched personal identifiers can improve Google and Meta matching for B2B retargeting use cases.

The main demo showed how to build niche prospecting workflows by combining public records, private enrichment providers, and social or intent signals.

The session showed how call transcripts can be cleaned, parsed into structured CRM fields, and written back to HubSpot through a workflow with validation and debugging steps.

Write the first run to a CRM or customer database, then schedule recurring scans or signal radars for job changes, social posts, new hires, or other relevant triggers.

The session opened with introductions from Deepline's team and an overview of the format, focused on data discovery and workflow improvements emphasizing reliability, observability, and shareability.

Data discovery combined public and proprietary sources, with a personal email enrichment example aimed at improving match rates on Meta and Google paid media, and a Nike.com example showing how different providers cover different datasets.

The CLI and provider categories were explained, including bring-your-own-key options and fallback strategies, and the clarification that customers own their Postgres database instances while Deepline logs provider success without storing or reselling customer data.

Workflow demos covered the org chart builder, version-controlled code and outputs, canonical company and lead tables feeding Postgres and CRMs read-only, and an example filtering HubSpot companies to automate outreach campaign additions.

Workflow runs provide detailed logs with code, inputs, outputs, and cost estimates. Few-shot prompting was recommended to improve accuracy, and niche signal scoring was shown differentiating winning versus losing accounts.

Provider testing ran sample tasks to evaluate coverage, pricing, and email validation, with a strategy of starting from public data sources and then adding proprietary data for completeness.

Workflows iterate until outputs stabilize, and public sharing lets others reuse and customize a pipeline without exposing PII. The Sentry integration was noted as in contracting, and cowork network egress setup was clarified.

Closing covered onboarding resources: new-user promo credits, product tutorial videos in the docs and on YouTube, and an open invitation to connect over LinkedIn, email, or Slack.

The session opened with a Deepline workflow run: a trigger fired, enrichment moved from pending to complete, source data was logged, and the underlying workflow was editable from a coding-agent environment.

The SMB enrichment section covered why small-business data needs sources beyond standard B2B databases, including Google Maps, Secretary of State records, owner lookup, phone validation, and email waterfalls.

The dental-office demo used recent-source research to find public and private datasets, including professional registries, state dental boards, local directories, reviews, booking systems, and job listings.

The workflow deployment section showed how a webhook can dedupe incoming leads, enrich company and contact data, score ICP fit, route results, and send Slack notifications from a cloud-run workflow.

The governance discussion separated workflow execution from downstream policy enforcement, especially for permissions, sending rules, and approval controls as more users create programmable GTM systems.

Workflow runs can be stored in a dedicated Postgres database so teams can inspect source data, sync clean records into CRMs, and reuse the same account and contact data later.

The workflow combines public sources, provider APIs, validation tools, and fallback logic so each step can be inspected and tuned for the market.

Start from a specific market, discover public and private data sources, then compare examples with sales outcomes to find signals that actually predict fit or intent.

The session started with niche SMB data problems, where public records, Google Maps, provider APIs, and owner lookup methods often need to be combined because no single B2B database has enough coverage.

The provider section compared skip tracing, company hierarchy sources, Apollo-style enrichment, PredictLeads-style signals, and public documents as inputs into a more resilient waterfall.

The platform walkthrough covered Deepline workflow primitives including visual task mapping, reusable components, sub-workflows, CSV mirroring, and provider endpoints for recruiting-style enrichment.

The event participant section covered public pages, social scraping, LinkedIn event groups, attendee portals, network calls, and prior-year participant lists as practical ways to infer likely attendees.

Many SMBs do not have clean websites, LinkedIn pages, or obvious executive records, so useful coverage often requires multiple public and proprietary sources.

It is an ordered sequence of providers and validation steps that tries cheaper or higher-confidence sources first, then falls back when data is missing or low quality.

Use public pages, social signals, LinkedIn event groups, attendee portals, and prior-year participant data, then manually validate when automation cannot reach reliable coverage.

The session opened with product maturity and readiness: fix the product before scaling enterprise deployments, especially when deploying engineers will rely on the workflow output.

The warm outbound workflow de-anonymizes US website visitors, filters for ICP fit, enriches LinkedIn and email data, posts a Slack approval step, then pushes approved leads into Lemlist.

Tenant URL technographics use known SaaS tenant URL formats, Bloomberg or cybersecurity seed data, and low-cost HTTP response checks to verify which companies actively use products like Salesforce or Zendesk.

Warm intro scoring ranks mutual connections by relationship strength and context. The best path is often a medium-strength relationship that is relevant and credible, not the most obvious connection.

LinkedIn event organizer scraping is constrained by login requirements, so the working pattern combines public extraction, Edges-style scraping, Apify actors, and fallback CSVs for pages that cannot be fetched reliably.

CRM cleanup and niche workflow building improve when the agent starts from explicit good examples, iterates against the real outcome, and treats Deepline as headless GTM infrastructure rather than another UI-only agent.

So this is one that I put together and this is running queries on a database. You can think of it as what are the key concepts they need to know. Instead of querying the data model every time, you define synonyms -- when someone says company, account, deal, opportunity, you have those mapped. You can define concepts consistently, add custom metrics, and the GPT always gets that definition first.

The hard part is building this, right? Now I have to go define every single concept, which isn't sustainable. So what we found is you give GPT the Salesforce table and the reports you're trying to generate, and say recreate this. What that captures is the metrics behind the definitions. You have to manually spot check, but for the most part that gets you 95% of the way there.

Do you do this in one running markdown file or do you separate semantic from a field reference?

Very, very separate. The semantic layer should work independent of the system prompt. You should be able to do something deterministic and programmatic on top of it. It's not free text. You're telling the system prompt to use it in the first step, but from then on the semantic layer is independent.

A lot of people are coming from the Clay world where everything is very structured. This is a model we used for scoring: build a big list, get potential accounts, have a good idea of your total addressable market, then try to get signals and enrichment about them. What's changing is you start with a small list of known good fits, score the features, and then expand from there.

Could you define small, in terms of volume?

Probably 30 to 40 is the minimum I've seen be actually useful and differentiated. Good results start happening around a hundred. It scales with what you're doing -- a restaurant company with 2 million targets needs maybe 10,000 before you differentiate, but for most products, starting with a hundred and guiding enrichment from what you find works well.

The data showed that west coast restaurants were converting higher. That's because the reps were starting early on the East Coast. By the time they got going, it was the beginning of the day for west coast people. So you have this confirmation bias -- 'oh yeah, west coast people answer the phone more.' That's not the causal driver. The data shows us something nobody would have articulated, which means all the east coast people weren't getting called in the morning.

Push to Lemlist and then it adds them to a campaign that I also designed with cloud code. Going from zero to warm outbound campaign took a couple minutes. That includes email validation, everything you need. This runs natively in our system with access to all integrations -- custom HTTP connectors, data providers, sequences, CRMs, or your data warehouse.

I want to target sales leaders going to major events like SuiteWorld or Dreamforce. If they participate, they're trying to find customers there but probably don't have before-event enrichment. If I can listen through signals of people going to these events, pull a list, enrich them, and give context as to why I'm reaching out, that's a good way to make the agent continuously scour for those people.

That's a perfect use case. We have an open-source managed agents implementation. The Anthropic managed agents work almost at Cloud Code levels of flexibility. It comes with a Slack interface built in so you can query any of these tasks. The reply bot finds additional contacts for multi-threaded outreach -- an SDR says 'I'm working on Nucor, find me other people within the account based on our ICP.'

Next time we do office hours, I'd be really interested in diving into the reply bot thing.

There are two ways to do signals. When a third party sends you information, create a webhook -- when this URL is hit with data, do some action. The trigger is external. The other way is schedule: every day, check for changes. You can hack a schedule to be a trigger. The difference is cost. We have a partner where you describe what you want and pay per update received, not per check. Like a penny per social post per person.

Do you do these often?

Yeah, weekly. Weekly feels like a lot so we might go biweekly. We have a Slack channel too -- Deepline CLI feedback on the website, cloud code plus GTM is right there.

It is a structured set of definitions, synonyms, and metrics that helps an LLM query business data consistently instead of guessing what each field means.

Start with a small set of known good accounts, enrich for differentiating signals, then expand once the scoring logic is producing useful distinctions.

Use webhooks when a third party sends a real-time signal. Use scheduled polling when the workflow needs to check for changes on a cadence.

The first thing to get right is what Deepline actually is. Claude Code is the interface. Deepline sits in the middle as one API. Claude asks for the outcome, Deepline figures out which of 30 to 40 data sources to use, and everything gets stored in a database so the workflow is auditable and reusable later.

That database layer matters more than people think. Three months later, someone can ask where a field came from, or take over the workflow, or build something new on top of the same account and contact records. In a vibe-coding world, owning the underlying SQL layer is what makes custom GTM tools possible.

For the org chart example, the model uses two kinds of information. First, what Claude already knows about how enterprise companies are usually structured. Second, the account-specific information the model would never know on its own, like titles, CRM notes, provider data, transcripts, and first-party context.

Would you be interfacing with this in Claude, or in Deepline itself?

The interface is Claude. Deepline is in the backend. If you wanted LinkedIn data, fundraising data, phone verification, or ad-spend data, you would normally go find different vendors and figure out how to use each one. Deepline aggregates those sources so you do not need twenty separate accounts just to solve one workflow.

The tactical workflow is simple: start from the outcome you want, describe what the output should look like, and let Claude find the information it needs. In this case the outcome was an org chart and a buying-committee view, not a data pull for its own sake.

One technique that keeps improving results is using recent-source research before you lock the workflow. If I do not know what a strong AE org chart or GTM playbook should look like, I can research recent discussions on Reddit, X, YouTube, and Hacker News, then use that as a starting point and iterate from there.

That recent layer matters because the model's built-in knowledge will always lag what changed in the last month. The recent research is not there because your company is on Reddit. It is there because the best implementation details often live in the open, and those are the details that close the gap between a generic workflow and a useful one.

The next problem is signal discovery. Most teams say they know what makes a good customer, but in practice they confirm their own bias. The better approach is to compare won accounts against lost or bad-fit accounts and ask what is actually different between the two.

The reason to focus on niche signals is that your ideal customer is never identical to a competitor's ideal customer. Even if two companies sell into the same category, one might skew more enterprise, another more mid-market. Their scoring models should not be the same.

Historically, teams built that mental model by talking to customers, reading websites, looking at hiring pages, and guessing. The problem is that manual signal discovery does not scale, and it often just reinforces what the team already believes. A won-versus-lost comparison is much harder to fool.

Once the signal set is useful, the next step is to operationalize it. You can build a scoring workflow, push it into the CRM, or run it on a schedule. The value is not just finding the signal once. It is turning it into an ongoing system that stays tied to outcomes.

How does pricing and packaging work? Is it monthly, usage-based, or something else?

The managed credits are pay as you go. The longer-term business is the backend database and infrastructure, not trying to nickel-and-dime every workflow action. If someone just wants enrichment and lightweight workflows, credits are enough. If they want the durable system of record underneath, that is where the real value lives.

If a customer already has a better enterprise contract with a provider like Apollo, they can override with their own API key. The point is interoperability. Use Deepline where it helps. Bring your own provider where you already have an advantage.

I want to find LinkedIn posts and comments by pain point, not just keyword, then keep monitoring relevant comments over time.

The raw data part is straightforward. The hard part is that LinkedIn itself does not give you sentiment search or vector search. So the workflow is usually: pull the raw comments or posts first, accept that retrieval cost, then have Claude group or filter the data by sentiment and relevance.

That logic applies more broadly too. Deepline is not trying to be the reasoning layer for every problem. It gets you the underlying data reliably. Claude Code does the interpretation, scoring, grouping, or orchestration on top of that data.

We also have a Clay-to-Claude Code migration skill. You copy the configuration of a Clay table into the skill, and it turns that into a workflow. The critical feedback for us is not whether it migrates a toy example. It is whether it preserves the real logic people depend on, or whether some workflow still forces them back to another tool.

If a Clay setup depends on a provider Deepline does not support directly, the migration flow tries to find the closest substitute. If there is no substitute, it should flag that instead of pretending the workflow is complete. That fallback logic is what makes migration credible.

So if I wanted to monitor a hundred accounts and track people by sentiment, is that a Deepline workflow or something Claude is orchestrating around Deepline?

That is the right way to think about it. If there were one API that gave you every data source you needed, that is Deepline. Claude Code orchestrates around it. Deepline stores the data, exposes the endpoints, and can run the workflow on a recurring basis, but the workflow itself is designed from the outcome backward in Claude.

That distinction matters because it keeps the stack clean. Deepline should be the infrastructure and data layer. Claude should be the interface and the reasoning layer. Once you see it that way, a lot of GTM workflows become simpler to design.