AI for marketing data and ad tech
Clean records, outside signals and scores your customers act on.
For marketing data, audience data and ad tech products. One record per account before any model touches it, public signals crawled into a searchable index, and scores written back where your customers work.
Built for: Marketing data and enrichment products, audience data platforms, B2B data products and ad tech portals.
Already built or tried AI? Get it scored with the AI Scorecard.
brindlepoint.example/accounts/quillmere
Quillmere Software · Tierly
DemoStep 1 of 5
Illustrative demo on a fictional agency. Not client work.
Where these products get stuck
What gets in the way, and how we handle it.
- 01
Conflicting records
Survivorship rules first
Match and merge rules make one record per account before enrichment or scoring.
- 02
Signals outside your data
Crawled into one index
Crawlers add page data, public follower counts and traffic rank to each record, searchable by your team.
- 03
Settings only experts can run
Guided, with validation
Dependent choices kept valid together, so customers finish a setup on their own.
AI inside your product
The modules and services that fit this kind of platform.
Each one is shown on a fictional agency. It is built into your codebase and ships under your name, on an engine we license to you.
What it connects to: Your CRM, billing, usage and firmographic sources, Salesforce write back, search and relational stores, and streaming logs. Sprints use sample accounts.
Tierlyby Datics AI
AI account scoring and tiers from your own customer history
Every account scored on how your best customers behaved, and the tier sent to the CRM.
Fits: Marketing data and enrichment products, B2B data platforms, revenue teams with several data sources
Marketing data engineeringby Datics AIService
Data engineering for MarTech and ad tech products
Crawlers, searchable indexes and streaming pipelines that turn outside signals into data your product can rank and score.
Fits: Any marketing, audience or ad tech product that ranks or scores outside data
Proof
What we have built that this rests on.
57 featured AI case studies: AI products and AI built into live products, each written up on our Work page.
NOLK Enterprises Inc.
Market data · Store intelligence
Thousands of online stores indexed from spreadsheets, compared by traffic and social following
The store list sat in four spreadsheets nobody could search. Now every store is indexed, enriched from the web and compared in dashboards.
B2B SaaS company serving professional firms
B2B data · Account scoring
We are building account tiers in Salesforce, scored from the company's own history
The same account carried conflicting values in five systems. We merged them first, then AI filled the gaps and scores came from real history.
An audience data company
Audience data · Streaming weblogs
Website logs stream through Spark into Elasticsearch and MySQL, ready for recommendations
An audience data company had website logs streaming in but needed them analyzed and stored for recommendations. We did it on its own stack.
How it works
Three ways to start, each at a fixed price.
Most marketing data products start with Tierly in a Build Sprint: master records for one source pair and a first scoring run.
MarTech Platform Product Review · 30 minutes, no chargeStart here. We look at your platform with you, agree the outcome you want for your customers, and tell you which of the three below fits.
Book a 30-minute Product ReviewNo AI yet, and a module fits
Build Sprint
$10,000 fixed3 weeks
Pick it when: One of our modules delivers the outcome you want.
- One customer outcome (2 to 3 related capabilities) working in your staging, behind a feature flag
- Pull requests to your repo, built on sample campaigns, synthetic comments and test accounts
- Quality and safety evals, the cost per customer and a packaging recommendation
- A fixed-price quote for production
Something new, or a plan first
Planning Sprint
$5,000 fixed2 weeks
Pick it when: You need something our modules do not cover, or want a plan before you build.
- Where AI fits across your platform, including beyond our modules
- A prioritized capability map, with what each one depends on
- A readiness review of your architecture, data, platform APIs and permissions
- A fixed-price quote to build it and take it to production
AI already built or tried
AI Scorecard
$5,000 fixed2 weeks
Pick it when: You have already built or tried an AI feature in your platform, live or not.
- One AI feature you have built, tested in staging on 50 of your own cases, with names and contact details removed
- Tests for invented numbers, scores that shift between channels, another customer's data and prompt injection from comments
- Cost per customer and a packaging recommendation
- A scorecard, ranked fixes, draft security answers and a fixed quote
Not a compliance audit or certification.
How the AI Scorecard worksProduction, at a fixed quote
We take what your Sprint built or planned live for your customers, by pull request, with no standing production access. It typically includes load testing at your busiest refresh and a spend cap per customer. Your Sprint or Scorecard fee counts toward it.
After go-live, optional support
The product is finished and yours to run. If you want us to stay on: a named lead engineer, a monthly quality and cost report, testing before any model or platform API change, and more capabilities, each at its own fixed quote.
Stop after any Sprint and keep what you got.
How a Build Sprint runs
- In the setup week we map your data, platform APIs and roles and agree what the Sprint touches.
- We follow your coding standards, tests and CI; your engineers review and merge every pull request.
- Half at signature, half when the Sprint Pack is delivered.
- Your time: about 2 to 3 hours a week from a product owner, plus an engineer to review pull requests.
Product improvements you can measure
What we agree to measure: quality and cost on test sets in the Build Sprint, adoption and outcomes once it is in production, so the decision to go further is made on evidence.
One record per account
Share of accounts with a single master record
Scores sellers use
Ideal accounts worked within a week
Data your team runs
Questions answered without an engineer
We set the success measures with you in the setup week.
Built for platforms that sell numbers
Controls your customers' security reviews ask about.
Each control is a design fact about how the module is built and how we work, not a compliance claim.
Metrics defined first
Every score is agreed with you before code, and means the same on every channel.
Inside platform rules
A collector per platform, inside its rate limits and storage rules, with sources you can swap.
People decide
Managers answer comments and account leads share reports; the AI ranks, scores and drafts.
We work within your social platform API terms, privacy requirements such as CCPA and GDPR, and the FTC rules your customers follow on endorsements. Compliance depends on the complete product, contracts, policies and operating environment; Datics does not present software engineering alone as legal certification.
How we handle social and audience dataQuestions we hear on the first call
Straight answers before you book.
Have you built ad tech?
Yes. We built a self-serve portal for streaming TV and digital billboard campaigns, with a guided setup, validation that keeps dependent choices consistent and a preview of the whole campaign before it is published.
Can you work on our existing data stack?
Yes. We have worked inside a running Kafka, Spark and Elasticsearch stack on its installed versions, and are working in a client's own Azure SQL and Microsoft Fabric.
Other martech platforms we build for
See it on your own platform.
Bring one product, metric or AI feature. In 30 minutes we tell you which of the three ways to start fits, what it would cost to serve per customer and what the platform APIs will allow.
Calls run in US Central time. No generic AI presentation: we discuss your product, users and production constraints.