AI and software for marketing teams, across content production, channels and languages, attribution, and the brand and claim rules you answer for.
Every answer on the right sits in a system you already pay for. Joining them is the work, and it is what makes the production argument hold.
In brief
EigenSpark builds AI systems and the software around them for marketing teams. We join the enquiry, the CRM record, the ad platform and the ledger, so cost per closed deal is a number you can open and check by campaign. We draft and adapt content in your own voice, across channels and into Indian languages, from the brand and product material you already own. We check claims and disclosures against your own rules before anything publishes. Then we hand it to your team to run.
Your systems
Nothing here asks you to replace your automation platform or your CMS.
Use cases
Production first, then channels and languages, then the numbers, then brand and claims.
Long form, email and social copy drafted from your product material and past approved work, so a first draft starts closer to publishable.
Generative AI →One approved message adapted into the formats, lengths and aspect ratios each channel needs, with the brand rules applied as it goes.
Generative AI →Campaigns adapted into the languages your market reads, reviewed by your own regional team, with product terms held fixed across all of them.
Generative AI →A signed brief turned into the landing page, the emails, the ads and the sales one-pager as a set, so the message stays the same across all of it.
Agentic AI →The questions your buyers actually ask, turned into content and structured data that answers them, so the pages get quoted as well as ranked.
AI & Data Strategy →One source taxonomy across ads, site, automation and CRM, so cost per lead and per closed deal open onto the records behind them.
Data Engineering & Platforms →Enquiries scored on fit and behaviour, enriched into a brief, and routed with the context a rep needs to make the first call useful.
Lead Enrichment Agent →What buyers actually object to on calls, read back into the messaging, so the next campaign answers the objection the pitch keeps losing to.
TabyGen →Every asset checked against your brand rules, your approved claim library and your disclosure requirements, with the rule it breaches named.
Continuous Compliance Monitor →Material produced in your name by distributors, partners and franchisees checked against the same rules, at the volume it actually arrives in.
Continuous Compliance Monitor →Competitor pricing, positioning and campaigns tracked from public sources into a weekly brief, with what changed since last week named.
Agentic AI →Registrations, attendance and questions read into a per-person summary, with a drafted follow-up ordered by what each person actually asked.
Document Intelligence Engine →Our own product
Our sales intelligence platform reads every sales conversation, so marketing learns which objections keep costing deals and which parts of the pitch reps drop.
How an engagement runs
Join the numbers first, automate the production, then hand it over.
Two to three weeks connecting your website, ad platforms, marketing automation, CRM and revenue data into one view per campaign, with one definition of a source and a lead. Everything else on this page reads better once this exists.
One workflow taken end to end: the asset kit from a brief, the channel and language variants, or the claim and disclosure review. Small enough to prove in a quarter.
Brand rules, tone, claim libraries, disclosure requirements and channel templates live in a console your marketing operations team runs. A new market or a new product does not need us.
Training
Ninety-minute hands-on sessions, run on your own brand material, campaigns and channels.
The marketing team
Drafting, adapting and reviewing with your approved work as the source, so what people learn is your voice and never a generic house style.
Walk away withA working prompt set for your own formats, and one campaign drafted in the session.
Marketing operations
Querying campaign, CRM and spend data directly, fixing the source taxonomy, and building the joins that make cost per closed deal possible.
Walk away withA corrected source taxonomy, and one report that no longer needs assembling by hand.
The CMO and brand owners
Which claims a model may never write, how disclosure rules apply to generated work, and how to review AI output at the volume it arrives in.
Walk away withA review standard for generated work, and the approved claim list that goes with it.
Marketing teams usually take AI & Machine Learning and Data & Analytics, with Leadership & Executive Readiness for brand owners. The full catalogue is in Training & Enablement.
The engineering half
Four layers of ordinary engineering. The first one decides whether attribution is possible at all.
Attribution is an identity problem before it is a modelling problem. Until a campaign and a person mean the same thing in four systems, every number is an estimate.
Your automation platform and your CMS stay the systems of record. Most of the effort is the ad platforms, the site, the CRM and the revenue data at the far end.
Built for the person whose name is on the campaign, with the draft, the source and the rule shown together, so anything can be corrected before it publishes.
Defined once and used everywhere, so the campaign report and the finance view of spend cannot disagree. Your team runs and owns it.
We also run the training that goes with it: SQL and data engineering, cloud, enterprise systems and cybersecurity, alongside the AI tracks. A marketing operations team that can query its own data stops waiting on a dashboard.
What we offer marketing teams
Most teams start with the production line or the attribution join, and use three of the four.
Where a marketing programme starts.
Where a single workflow starts.
AI tracks, and the foundations under them.
Where the specifics change.
The specifics change by sector. See how this lands in Retail & CPG or Real Estate & REITs.
Security and controls
A marketer approves everything, claims come from your own library, and consent travels with the contact.
Every asset, post, email and page goes to a person on your marketing team before it is live. The system drafts, adapts and queues.
Product claims, comparisons and numbers are drawn from the approved library your team maintains. Anything outside it is flagged for a person to write.
Paid partnership disclosure, endorsement rules and your own regulatory requirements are held as configuration your team edits, with the rule named on each flag.
What each person consented to, on which channel and when, is held with their record and enforced at send time, inside your own systems.
FAQs
It sounds like you to the extent that you can show us what us means. We build from your own approved work: past campaigns, product material, the pages that performed, and the words your customers use back to you. A marketer edits the first drafts, and those edits go back into the rules. The first fortnight is usually spent on voice, and it is the part that decides whether anyone uses the output.
No. Your automation platform and your CMS stay the systems of record. The work is the production line that feeds them, the join between them and the CRM, and the review step before anything publishes.
By fixing the taxonomy before the model. One definition of a campaign and a source, applied across the ad platforms, the site, the automation platform and the CRM, with the person and account records matched. After that a cost per closed deal is arithmetic. Before that, an attribution model mostly formalises the mess.
It checks them against your rules and shows the rule behind each flag. Two things drive that in India: the CCPA guidelines on misleading advertisements and endorsements, which require any material connection to be disclosed, and the ASCI influencer guidelines, updated in 2026 to cover AI generated influencers. We hold both as configuration your team edits. The judgement on a borderline claim stays with your legal and brand people.
Yes, and it needs your regional team in the loop. We hold product names, claims and regulated wording fixed across every language, adapt the rest, and route each version to a reviewer who reads it. Where a language is thinly served by the models, we tell you that before you plan a campaign around it.
TabyGen is our sales product, and marketing is the second audience for what it hears. It reads sales conversations, so it can tell you which objections keep costing deals and which parts of the pitch reps abandon. That is the most useful message research you already own. It has its own site at tabygen.com.
No, and the parts that are not are usually what makes the AI work. The source taxonomy, the identity matching, the asset storage and the review workflow are ordinary engineering, and they are most of the effort. The same is true of the training: alongside the AI tracks we run SQL, data engineering, cloud, enterprise systems and cybersecurity.
The production work pays for itself with two or three people, because it is turnaround time on the same brief. The attribution work needs enough campaigns and enough spend to be worth joining, which in practice means a budget where a wrong answer costs more than the join.
Related
The services this is built from, the products it ships as, and where the leads go next.
The production side: drafts, variants and languages.
→ ServiceThe join that makes attribution possible.
→ ProductEnquiries researched into a deal-ready brief.
→ ProductOur sales product, on its own site at tabygen.com.
↗ IndustryMany SKUs, many channels and packaging rules.
→ PillarThe engineering side, for the parts we build for you.
→Send the report, plus the CRM export and the ad spend for the same period. We call you within 48 hours and go through which numbers can be joined today, which cannot and why, and where the production time is actually going.
Talk to us