MarTech software development for data platforms
Your marketing platform, with AI that measures fairly and ranks what matters first.
For social listening, influencer, ad tech and marketing data products with live customers. We build the data layer and the AI on top of it, inside your product, under your name.
- One score that means the same on every channel
- A person shares every report and answers every comment
- Collected within each platform's rules
Three ways to start, from $5,000: Build Sprint · Planning Sprint · AI Scorecard
app.murmurline.example / halyard-finch / campaigns
Halyard and Finch · Campaign reports
AI scoredMoodly by Datics AI · Try it step by step →
Illustrative demo on a fictional agency. Not client work.
Who it is for
Built for established MarTech and ad tech platforms.
Each page leads with the modules that fit that kind of platform, the questions its buyers ask and where most teams start.
Building another kind of marketing software? Tell us what it measures, and we tell you plainly whether we fit.
Book a 30-minute Product ReviewWhy AI stalls in marketing platforms
What gets in the way, and how we handle it.
- 01
Numbers that mean different things per channel
One scale, defined first
We agree what each metric means with you before code, then score every channel on the same 0 to 100 scale.
- 02
Sentiment models that misread social language
A model for each signal
Dedicated models for sentiment, emotion, toxicity and sarcasm, with a larger model kept for summaries.
- 03
Platform APIs you do not control
Platform rules as product rules
A collector per platform behind one registry, storage rules in scope and quotas spent on new data only.
- 04
Inboxes buried in noise
Ranked for a person
Comments ordered on who wrote them and what they wrote, while the manager decides every reply.
- 05
The same account in five systems
Clean records before AI
Match and survivorship rules make one record per account before any model fills or scores it.
Why AI earns its place in your product
Sell the numbers
Metrics agencies can defend.
Scores that mean the same on every channel, with the comments behind them.
Save managers hours
The right comments first.
A ranked queue instead of three platforms read end to end.
Keep margins
Quota and AI cost in view.
New data fetched once, refreshes throttled, and API quotas and AI cost tracked for the operator.
Modules by Datics AI
Start from AI we have already built, not from zero.
MarTech AI you can see before you buy: each module runs on a fictional agency and builds on data and AI work we have done for social, influencer and marketing data products.
Paste a campaign's posts and see how the audience feels, on one scale across every channel.
Fits: Social listening and influencer analytics platforms, campaign reporting tools, agency software
Social managers see the comments worth answering first, across every platform.
Fits: Social media management platforms, agency inbox and reporting tools, community management software
Campaign data from every platform, fetched within its limits and kept within its rules.
Fits: Social listening, influencer and social analytics platforms, any product built on social APIs
Influencer teams see which creators fit a brand brief, with the reasons behind every score.
Fits: Influencer marketing platforms, creator discovery tools, agency influencer software
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
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
Moodlyby Datics AI
AI sentiment analysis for social and influencer campaigns
Paste a campaign's posts and see how the audience feels, on one scale across every channel.
Fits: Social listening and influencer analytics platforms, campaign reporting tools, agency software
Ranklyby Datics AI
AI comment ranking for social media management inboxes
Social managers see the comments worth answering first, across every platform.
Fits: Social media management platforms, agency inbox and reporting tools, community management software
Channellyby Datics AI
Social data collection within platform API rules
Campaign data from every platform, fetched within its limits and kept within its rules.
Fits: Social listening, influencer and social analytics platforms, any product built on social APIs
Scoutlyby Datics AI
AI creator matching and vetting for influencer marketing platforms
Influencer teams see which creators fit a brand brief, with the reasons behind every score.
Fits: Influencer marketing platforms, creator discovery tools, agency influencer software
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
Also for your product
Your product, inside Claude and ChatGPT.
We build a connector so your users can ask their own AI assistant about their data in your product, each signed in with their own login and seeing only what they can already see. It starts read-only, only the fields you approve leave your product, and actions each user approves can come later.
How we build connectorsHow did comment sentiment on our spring campaign change this week?
Mostly up, with one creator's post drawing the complaints. Here are the posts behind it.
Proof
Our MarTech work.
Data and AI we have built for SenTrac, Sparrow Marketing, NOLK and an audience data company, and are building for a B2B SaaS company.
57 featured AI case studies across our work
SenTrac
Social listening · Campaign sentiment
Agencies see how audiences feel about a campaign, scored fairly across six channels
Agencies copied comments by hand and misread sarcasm and emoji; now SenTrac scores every comment and compares channels on one scale.
Sparrow Marketing
Social inbox · Comment ranking
Social managers see the comments worth answering first, learned from their own choices
Managers dug through three platforms for the comments that mattered; now one ranked queue puts them on top and sets junk and spam aside.
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.
More MarTech work
SenTrac
Social data · Collection within API rules
Campaign reports use only the data each platform allows, refreshed within its limits
Social APIs are gated, capped and change often; we built SenTrac's data layer so each platform's rules and limits hold and reports stay current.
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.
“Umair and his team at Datics have done an outstanding job developing SenTrac, our AI-powered sentiment analysis platform. What I have appreciated most is their willingness to understand our vision, work through complex requirements, and incorporate feedback as we refine the platform.”
Jason Pampell · Managing Director
See the work
How it works
Three ways to start, each at a fixed price.
A 30-minute review tells you which one fits. Whichever you pick, you know the production price before you commit, and your fee counts toward it.
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.
Moodly · Rankly · Channelly · Scoutly · Tierly
- 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.
Need integrations first? Marketing data engineering covers crawlers, indexes and streaming data. Ask in the Product Review.
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 dataIn AI and data since2018
- AWS: Select Tier Partner
- Anthropic: Partner · Claude models
- CopilotKit: Agent user interface
- LangGraph: Production agent engineering
- Databricks: Governed AI & agent workflows
- Cursor: Agentic development environment
- Microsoft Azure: Production AI delivery
- Google Cloud: Production AI delivery
AI shipped inside products for
Questions we hear on the first call
Straight answers before you book.
The questions MarTech product and engineering leaders ask most.
Do you build AI for marketing agencies?
We build for the software agencies run on: social listening, influencer, social media management, ad tech and marketing data products. The AI ships inside that product, under its name, for every agency on it.
Do you write marketing content with AI?
That is not our focus. We build the data and measurement side: collection, sentiment and emotion scoring, ranking, enrichment and account scoring. Summaries are drafted only beside the numbers, for a person to edit.
How do you make sentiment scores comparable across channels?
We agree what each number means before code. Each comment's positive, neutral and negative shares become one 0 to 100 score where 50 is neutral, and reactions are scored on their own, so a missing reaction never pulls a post down.
How do you handle platform API limits and terms?
We map what each platform returns and permits before designing reports. Each platform gets its own collector, refreshes fetch only what is new, and storage rules such as removing deleted posts are part of the scope.
Can the AI reply to comments or share reports on its own?
No. It ranks, labels, scores and drafts summaries. A manager answers every comment, and an account lead edits and shares every report.
Do you need our customers' data in the Sprint?
No. Sprints run on sample campaigns, synthetic comments and test accounts in your staging, and code reaches your product as pull requests.
How do you choose a MarTech software development company?
Look for a team that has built on social platform APIs and their limits, scored messy social language and shipped data products non engineers run themselves. Ask how a metric is defined, what happens when an API changes and who owns the code. We answer each of these in the Product Review, with the work behind it.
Who owns the code?
What we build for your product lives in your repository and ships under your name. The reusable engine underneath stays ours and is licensed to you; the terms are in your contract.
Do you have SOC 2?
Not yet. We work in your staging by pull request, under your accounts, and in production only through access you grant and can revoke.
Where are you based?
Datics is headquartered in Austin, Texas, with a named lead engineer and US-hours overlap on every engagement.
MarTech insights
How we think about the hard parts.
Answers for product and engineering leaders, from the team that builds it.
Brownfield AI
How do you build an AI data pipeline from outside sources?
An AI data pipeline for outside sources needs a reviewed data model and ETL that links records to real entities. Learn how to plan sources and mappings.
Rana Umar Majeed · Sep 9, 2026Brownfield AI
How do you build a data ingestion pipeline for many sources?
A data ingestion pipeline for many outside sources starts with a small core schema and written field rules, then delivers data in checked stages.
Rana Umar Majeed · Oct 27, 2026Brownfield AI
AI data quality: how do you clean and deduplicate data?
AI data quality comes before prompts: normalize data per tenant, deduplicate records with explicit rules, and keep facts such as prices out of the model.
Rana Umar Majeed · Sep 16, 2026Brownfield AI
Entity matching: how do you map supplier data to a catalog?
Entity matching for product data works when you agree on mapping rules first, normalize before you compare, and route uncertain matches to people.
Rana Umar Majeed · Nov 2, 2026AI agents
Where should human in the loop AI review happen?
Human in the loop AI works when review happens field by field, before anything ships. Learn where to place checkpoints and how to measure accuracy.
Rana Umar Majeed · Oct 16, 2026AI in production
AI evaluation: build your test set before the first release
AI evaluation starts before launch: collect real cases from your product, define pass and fail for each one, and run the set on every prompt or model change.
Umair Majeed · Oct 26, 2026See 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.
Own several MarTech products? Datics for private equityCalls run in US Central time. No generic AI presentation: we discuss your product, users and production constraints.