Elevon in ForbesA Weekly Read on What the Country Is Buying, With Every Claim Sourced
Four channels of market signals become one sourced report on what the country is living right now and what it means for the bank.
Client
Our banking client
Industry
Banking
Solution
Custom AI agent (Elevon suite)
Deployment
Production
Market and attention radar with memory between runs.
The Challenge
Every week the bank wanted a clear read on what people in the country were talking about, worrying about, and buying, and what that meant for its own products. The signals existed across competitive, attention, demand, and macro channels, but nobody had time to read all of them, cross-check them, and turn them into something a team could act on.
A manual weekly summary had two failure modes we did not want to inherit. It forgot what it said last week, so nothing could be tracked over time, and under deadline pressure it quietly filled gaps with plausible-sounding claims that had no source behind them.
Why Elevon
We built it as a working aid, not a dashboard toy: something the bank's teams open on Monday, trust because every line points back to a source, and can compare against last week because the system actually remembers.
One report, four channels
Competitive, attention, demand, and macro signals merge into a single HTML report: buzz topics, five areas of personal finance with indicators, growth topics, a seasonal map, a brand section, and a methodology plus source register.
Memory between runs
A topic registry and an append-only trend ledger with a dedup key let the report remember what it already covered and diff week over week: new topics, and topics that are growing, the movers.
An admitted gap beats a pretty lie
A channel with no data this run renders as Preparing. A topic with no live observation is marked model-only, no live data this run, with zero votes. Every claim carries its source link, and a quality badge computed in code counts anything without one.
“The rule was simple: an admitted gap beats a pretty lie. If a channel is empty this week, the report says so, out loud, instead of guessing.”
The Rollout
We started from the scraping service the bank already trusted and kept the four channels intact end to end. Thin normalization agents rewrite each scraped record one-to-one into a common shape; they do not filter, rank, or summarize, so nothing is lost or spun before the analysis even begins.
On top of that we layered detection, diff, and fusion, wired the stateful memory in, and put the quality badge in code rather than in a prompt. The report ships weekly as HTML together with the new version of the trend ledger, so each run leaves the system smarter than it found it.
What this looks like in practice
The fusion agent ties any product takeaway strictly to the bank's own product catalog. It reads the signals and connects them to offers that already exist; it never invents an offer to make a topic land better. The quality badge is deterministic and lives in code, so no run can talk its way to a clean score.
The suite
Signals in, normalized
Detect, diff, remember
Fuse and publish
Illustrative reconstruction of the production suite.
The Results
The bank got a radar its teams open on their own, without anyone chasing them, because it is fast to read and easy to trust.
One sourced report every week, replacing a manual read that never got done twice the same way.
Week-over-week continuity: new topics and movers surface automatically instead of being remembered by someone who happened to be around last Monday.
Every claim links to a source, and the quality badge makes any unsourced item visible instead of letting it hide.
Product takeaways stay honest because they can only reference offers that actually exist in the catalog.
Empty channels and model-only topics are labeled as such, so teams know exactly how much weight to put on any given week.
Hours saved / week
manual reading and cross-checking removed
100% sourced
every claim carries a source link
Week-over-week memory
new topics and movers tracked automatically
Figures are illustrative and shown as an example of the shape of the impact. Replace them with the client's real numbers before publishing.
“It remembers what it already knew, points to a source for everything it claims, and would rather admit a blank than fill it with a guess. That is why the teams actually read it.”
Why It Worked
We put the discipline where it cannot be argued away. The quality badge is computed in code, sources are mandatory, and product takeaways are bounded by a real catalog, so trust is a property of the system rather than a promise about the model.
And we let the system carry its own memory. A topic registry and an append-only ledger mean each run stands on the last one, so the report gets more useful the longer it runs instead of starting from zero every Monday.
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