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Marketing & MarTech

Marketing technology that reads the room and the data

Brand-safe content generation, WhatsApp and email campaign automation, ad-to-conversation attribution and analytics that connect spend to revenue.

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Use cases

Where AI pays off in Marketing & MarTech

01

Content generation

On-brand drafts, variants and localisation with review workflows.

02

Campaign automation

Segmented, scheduled journeys on WhatsApp, email and ads with measurement.

03

Attribution & analytics

Pipelines and dashboards that tie campaigns to conversations and sales.

45-60%Click rates on targeted WhatsApp broadcasts
1 viewOf spend to revenue across channels
Services

How we help

AI Agents & Automation

Multi-agent systems, copilots and workflow automation that hold up in production.

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Machine Learning & Data Science

Predictive models, NLP, vision and MLOps with measurable business lift.

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What we solve

Problems we solve in Marketing & MarTech

Each maps to a reference architecture and to outcomes the industry has documented.

WhatsApp commerce agent

Sales conversations stuck on personal phones

Orders, questions and promotions run through WhatsApp with no history, no automation and no way to see what drove revenue.

How the system works

  1. Ad or QR opens chat
  2. AI agent answers from catalogue
  3. Cart & payment in chat
  4. Order synced to store
  5. Campaign & follow-up
WhatsApp Business PlatformShopifyRazorpay / StripeRAG over catalogueTeam inbox

Outcome: 53% of retailers already automate messaging on the WhatsApp Business Platform (Infobip), and the same grounded-agent pattern turns that channel into a store, a support desk and a campaign tool with attribution. Infobip WhatsApp statistics

Architecture
Analytics copilot on a governed semantic layer

Every question needs an analyst

Leaders wait days for a report, dashboards disagree with each other, and the data team is a queue. Raw data sits in the warehouse without becoming a decision.

How the system works

  1. Raw sources
  2. Lakehouse with partitions & quality checks
  3. Semantic layer of metrics
  4. Text-to-SQL agent
  5. Dashboards & alerts
Iceberg / DeltadbtSnowflake / BigQuery / DatabricksCube / dbt Semantic LayerGenie / Cortex Analyst

Outcome: In dbt's own test, questions answered through a governed semantic layer reached 83% accuracy, against a 16.7% raw-SQL baseline in the data.world benchmark it replicated; vendors report time-to-insight falling by over 99% with 62% adoption within a year. dbt: the semantic layer as the data interface for LLMs

Architecture
Sales agent with CRM tools

Leads that go cold before anyone replies

Inbound leads wait hours, reps spend their time on CRM hygiene, and nobody knows which deals deserve attention this week.

How the system works

  1. Lead arrives
  2. Enrich from web & CRM
  3. Qualify & score
  4. Reply or book meeting
  5. Log to CRM & alert rep
HubSpot / SalesforceWeb enrichmentTool callingWhatsApp / emailGuardrails

Outcome: AI-agent adoption in customer-facing service organisations rose from 39% to 66% between 2025 and 2026; the same grounded-agent pattern applies to first response and qualification in sales. Salesforce research via Agentforce ecosystem guide

Architecture

Let's build intelligent systems that drive growth

Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.

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Talk to experts about your product idea

Every great partnership begins with a conversation. Whether you are exploring possibilities or ready to scale, tell us what you are actually trying to build.

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