u/snowingbol

We had one big ETL job running every month and refreshing pretty much everything.

Started looking at the numbers and realized most of the records hadn't changed.

Meanwhile, the stuff we actually cared about could already be out of date. Someone changed jobs right after the refresh and we're stuck with the useless record.

We've moved those records to change-based updates and left the slower stuff on the monthly schedule.

Coresignal has been pretty good for keeping the company and employee data fresh without having to reload everything.

Much less data moving around for no real reason.

Now we're just working out which records actually deserve the faster treatment.

We used to run one big enrichment job every month.

Pull everything, update everything, done.

The problem was that some data changes a lot faster than other data. Someone can leave a company a few days after the refresh, but their old company would stay in our CRM for another few weeks.

We changed it so things like employer and job title get updated when they change. We've been using Coresignal for the fresh company and employee data and it's worked well for that.

Other fields don't need this. Founding year, for example, isn't going to change anytime soon.

So now we're basically just leaving the boring fields alone.

We used to run one big enrichment job every month.

Pull everything, update everything, done.

The problem was that some data changes a lot faster than other data. Someone can leave a company a few days after the refresh, but their old company would stay in our CRM for another few weeks.

We changed it so things like employer and job title get updated when they change. We've been using Coresignal for the fresh company and employee data and it's worked well for that.

Other fields don't need this. Founding year, for example, isn't going to change anytime soon.

So now we're basically just leaving the boring fields alone.

I've ended up with both and I'm not sure that's actually better.

Some things are still easier to handle directly through an API. Others make much more sense as tools the agent can discover and call itself.

I tried Coresignal's MCP recently and it convinced me to move a couple of data workflows over. The OAuth setup alone was nicer than keeping another API key in a config file.

But now I have this weird hybrid setup where some data comes through MCP, some through direct API calls, and some through our own tools.

It works, but the architecture is starting to look like it was designed by three different people who never met.

Is there an actual rule you use for deciding whether something should be an MCP tool or just stay an API call?

I'm using both and I'm not sure that's actually better.

Some things are still easier to handle directly through an API. Others make much more sense as tools the agent can discover and call itself.

I tried Coresignal's MCP recently and it convinced me to move a couple of data workflows over. The OAuth setup alone was nicer than keeping another API key in a config file.

But now I have this weird hybrid setup where some data comes through MCP, some through direct API calls, and some through our own tools.

It works, but the architecture is starting to look like it was designed by three different people who never met.

Is there an actual rule you use for deciding whether something should be an MCP tool or just stay an API call?

One thing we've noticed is that research is usually the first thing to get skipped once reps start working through bigger lists. We've been trying to automate that step so everyone at least has some context before the first email goes out.

Right now, whenever a contact enters an Outreach sequence, an n8n workflow kicks in. It pulls fresh company data from Coresignal and feeds it into NotebookLM to generate a short research brief. Things like "the company just opened a London office," "they've been hiring SDRs for the past two months," or "they recently migrated from HubSpot to Salesforce" tend to make the summaries much more useful than generic firmographics. The finished brief gets added to the Salesforce activity feed before the sequence starts.

The whole process takes about 90 seconds, which is fine for us. The bigger issue is the summaries themselves. Most are useful, but every now and then NotebookLM latches onto something that's technically true but doesn't really help with personalization.

I’m interested if anyone has tried a similar approach. Are you using a different model for the summaries, changing the prompts, or filtering the data before it gets sent to the LLM?

We've been trying to solve a pretty specific problem. The agent needs to answer detailed questions about a company, but loading the full company profile into the prompt eats up context fast. Summarizing everything upfront helps with token usage, but it also leaves out the details that sometimes matter most.

Right now we're pulling only the fields that seem relevant to the question instead of fetching the whole record. Company data comes from Coresignal, historical context sits in Pinecone, and LangChain handles the retrieval flow. The data stays current enough that we're not running our own sync layer for it, which has been one less thing to maintain.

However, we’re unsure what to retrieve before the query runs. We built a lightweight classifier that predicts which fields are needed based on the question. It does well for common requests, but once the questions get more open-ended, it starts making the wrong calls.

Not sure what’s the better way to handle this. Are you letting the LLM decide what to fetch, using metadata to narrow the search first, or taking a completely different approach?

The sales team keeps flagging that records are out of date. Contacts who've changed roles, companies that have restructured, accounts that no longer fit the ICP but are still in active sequences.

Tested a few options. Apollo is solid with contact coverage but the company record freshness has been inconsistent in testing. PDL has better raw volume but the dataset refresh cadence on company records hasn't been tight enough. Coresignal has been a more reliable option for data freshness so far.

Has anyone built something more automated here? Interested in what the trigger logic looks like when it actually works.

Rebuilt our ICP criteria earlier this year and the list looked great on paper. Three months into running campaigns against it, and we found some cracks. Funding stages a round behind, headcount figures frozen in time, a couple of acquired companies still listed as independent.

Tested a few providers. PDL has solid volume but refresh timing was an issue. ZoomInfo has broader coverage but the company data freshness wasn't meaningfully better for the price jump. Coresignal held up the best on fresh data, company and employee records were noticeably more current in side by side testing. Running it through Clay for the enrichment workflow which made the integration a lot less painful than expected.

Coverage still gets thinner in some verticals regardless of provider, which can still cause some headaches.

How are others approaching TAL refresh? Wondering if anyone has built something more automated or if manual review cycles are just the reality.

We hit the wall on response times and couldn't keep up with API changes, so we moved off what we had. At the moment, we use Coresignal, freshness was better than what we'd tested, and the full work history on profiles turned out to matter more than we expected.

Still early, so reserving judgment. Wondering what others here are running and how the switch went for you.