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AI that works in production. Not in demos.

Most "AI for marketing" is a wrapper around ChatGPT. Real AI systems perceive state, plan actions, use tools, and recover from failure — automatically, on schedule, without a human in the loop. That's what we build.

Book an AI Architecture Review →

Three reasons AI doesn't survive contact with reality.

These aren't technical edge cases. They're the same three problems, every time.

Context is bad

The model is fine. The prompt is the problem. Bad context = hallucinated insights = AI your team stops trusting after week two.

No write-back

Your AI surfaces an insight. Then what? If there's no action layer, you've built an expensive dashboard with worse UX.

No observability

You don't know when the agent fails, why it gave a wrong answer, or how to improve it. It's a black box you're paying to run.


Production AI systems for marketing teams.

Not prototypes. Systems with context architecture, observability, and write-back actions built in from day one.

01
Agentic reporting workflows
Agents that monitor campaign performance, detect anomalies, and generate narrated insight summaries — on schedule, without human assembly.
02
Natural language querying
Connect your warehouse to an LLM so analysts and executives can ask questions in plain English and get defensible, sourced answers.
03
Context architecture (CaaS)
Design the context layer that feeds your agents: what customer state, campaign state, and business rules the model needs to reason correctly.
04
AI observability
LLM call logging, prompt versioning, output quality monitoring. Know when your agent is underperforming before your CMO notices.
05
AI stack design
Audit your current stack for AI readiness. Select the right orchestration framework. Get a blueprint, not a vendor recommendation.

The stack we design and build.

Every layer has a job. Every job has observability. Nothing is a black box.

01
Data Core
Snowflake / Databricks
02
Semantic Layer
dbt / Cube
03
Context Layer — CaaS
what the agent knows
04
Agent Runtime
perceive → plan → act → observe
05
Write-back Actions
campaign pauses, budget shifts, alerts
06
Observability
Helicone / LangSmith / Arize

The tools we build on.

Claude AILangChainPythondbtSnowflakeHeliconeLangSmithAirbyteKafkaRedisTemporal

At TapClicks I built production agentic AI systems — perceive-plan-act-observe loops with real write-back actions — not proof-of-concepts. Ask Your Dashboard (NLQ agent), AI Operator Agents, and ObsGap (AI observability) shipped to real customers.

AR
Angshuman Rudra
Founding PM, TapClicks · 10 years

Start with an AI Architecture Review.

Two hours. We assess your current stack, identify where AI can replace manual work, and design the agent architecture. $2,000 flat.

Book a review →

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