CLICKFORCE cuts marketing research from weeks to under an hour with AI agents and Text-to-SQL

How CLICKFORCE used AI agents and Text-to-SQL to cut weeks of marketing research to under an hour

CLICKFORCE turned a multi-week research process into a sub‑hour workflow, giving marketers fast, data-backed answers instead of generic, slow-moving reports. That business pain—long lead times, fragmented data, and LLMs that sounded smart but weren’t actionable—drove the creation of Lumos, an AI-powered industry analysis platform built around AI agents and grounded models.

Executive snapshot

  • What happened: CLICKFORCE automated industry analysis with AI agents, vector retrieval, and automatic Text‑to‑SQL, producing reports in under an hour.
  • Why it matters: Faster insights, lower external research spend, and self‑service analytics for brand owners, agencies, and marketers.
  • Reported impact: Analysis time compressed from 2–6 weeks to under an hour and operational cost reductions of about 47% (reported by CLICKFORCE).

The problem: confident language models, weak business answers

Large language models (LLMs) can generate fluent text, but out-of-the-box outputs often lack the domain specifics marketers need. CLICKFORCE faced three core problems:

  • Fragmented data across CSVs, vendor reports, and internal docs meant analysts spent time stitching context together.
  • Manual workflows produced reports in two to six weeks—too slow for fast marketing cycles.
  • Generic LLM recommendations sounded plausible but didn’t always map to verifiable data or executable actions.

Solution overview: Lumos — grounded AI agents for marketing analysis

Lumos combines AI agents (software that coordinates steps for an LLM to take), grounded retrieval over curated documents, and automatic Text‑to‑SQL (automatic translation of plain-language questions into database queries). Rather than trusting a model to invent facts, Lumos forces answers to cite and run against the company’s own datasets.

CLICKFORCE turned to AWS to replace slow, manual analysis with a next‑generation AI marketing analysis solution built on Amazon Bedrock and SageMaker.

How AI agents and Text‑to‑SQL power marketing analysis

At a high level the system follows three stages: ingest, retrieve, and answer. Here are the core mechanics, explained plainly:

  • Ingest: Source documents (campaign reports, vendor PDFs, CSV exports) are stored and converted into searchable form. Numeric representations of documents—called vector embeddings—act like numerical fingerprints that let the system find the most relevant documents quickly.
  • Retrieve: AI agents decide what evidence is needed. Retrieval pulls matching passages through vector search so the language model can reason with real, contextual data instead of hallucinating.
  • Answer: When a concrete metric or table is needed, the system generates a structured action (JSON) that is translated into SQL and executed against up‑to‑date databases. Results are returned with provenance so the user can see where each claim came from.

In plain terms: three steps

1. Collect & index: Documents and CSVs are stored and turned into searchable fingerprints.

2. Find & reason: An AI agent pulls the most relevant documents and decides whether to run a query.

3. Query & cite: Natural language questions are converted into SQL, executed safely, and returned with citations.

Technical appendix (overview for engineers)

The implementation at CLICKFORCE is AWS‑centric but the pattern is portable: any cloud or hybrid stack with a vector store, model-serving layer, ETL, and secure query execution can reproduce the pattern.

  • Agent & model layer: Amazon Bedrock Agents orchestrate reasoning and retrieval; foundation models are tuned and validated with Amazon SageMaker.
  • Retrieval & indexing: Documents live in Amazon S3 and are embedded into vectors, then indexed for fast retrieval in Amazon OpenSearch Service (vector search).
  • ETL & databases: AWS Glue crawlers ingest CSVs into SQL stores and keep tables current.
  • Execution & plumbing: User queries hit a Streamlit front end on Amazon ECS behind an Application Load Balancer. AWS Lambda functions invoke Bedrock Agents and translate agent actions into SQL via a Text‑to‑SQL pipeline. MLflow tracks experiments and model versions used for Text‑to‑SQL validation.

Important safeguards implemented:

  • Schema validation and parameterized queries to prevent malformed or unsafe SQL.
  • Sandboxed query execution and read‑only roles to protect production data.
  • Experiment tracking (MLflow) and SageMaker‑led tuning to reduce translation errors from natural language to SQL.
  • Provenance metadata attached to each answer so users can verify the sources behind recommendations.

Example flow (conceptual)

Natural request: “Which SKUs grew fastest in Taiwan Q3 vs Q2, and which channels drove the lift?”

Agent decision: Retrieve recent campaign reports + sales CSVs. Agent emits an action: identify top SKUs and compute channel attribution.

Text‑to‑SQL translation: a validated SQL query is produced and executed against the up‑to‑date sales table, returning a ranked list of SKUs and channel breakdowns. The result is presented with links to the source reports and the query that ran.

Foundation models alone produced generic recommendations; grounding those models in industry data was necessary to create actionable intelligence.

Business impact: faster, cheaper, and more democratic insights

  • Speed: Industry analysis that once required two to six weeks now completes in under an hour.
  • Cost: CLICKFORCE reports roughly a 47% drop in operational costs by cutting third‑party research spend and automating analyst time (reported by CLICKFORCE).
  • Access: Brand owners, agencies, analysts, marketers, and media partners can self‑serve reports—accelerating decisions and pitches.
  • Trust: Grounding outputs in curated corpora reduced hallucination risk compared with generic LLM outputs.

Trade‑offs, governance, and what to watch

This approach delivers value, but it introduces responsibilities:

  • Vendor lock‑in & costs: An AWS-centric stack simplifies implementation but may create long‑term dependency. Recurring costs include model inference, vector search calls, storage, and ETL jobs. Optimize through model selection, batching, and caching.
  • Data governance: Implement strict access control, encryption in transit and at rest, and audit logs. Sensitive PII must be filtered before indexing into knowledge bases.
  • Drift & retraining: Monitor model and retrieval drift. Track metrics like Text‑to‑SQL accuracy, failed queries, and downstream business KPIs. Schedule retraining or re‑indexing when performance degrades.
  • Query safety: Enforce schema checks, parameterization, and safe execution patterns to prevent injection and accidental destructive queries.
  • Human‑in‑the‑loop: Keep analysts in the loop for validation, especially for strategic recommendations and contract‑sensitive decisions.

By combining Bedrock Agents, SageMaker, OpenSearch, and Glue, CLICKFORCE converted a weeks‑long manual workflow into an automated process that runs in under an hour.

How to start — a practical checklist for leaders

  • Identify a high‑value, repeatable workflow (e.g., monthly industry reports, pitch decks, campaign post‑mortems).
  • Audit existing data sources: Which files, reports, and databases are authoritative? Clean and centralize them first.
  • Choose a retrieval stack: Vector DB + embeddings for documents; ensure you can attach metadata for provenance.
  • Prototype Text‑to‑SQL: Start with a small schema and test extensively. Track errors and tune with a model or small labeled dataset.
  • Enforce governance: Role‑based access, encryption, query sandboxing, and logging from day one.
  • Measure ROI: Baseline analyst hours, third‑party spend, and report turnaround time so you can quantify improvements.

Key takeaways & FAQs

  • How did grounding reduce hallucinations?

    Grounding uses curated knowledge bases and vector search so the agent retrieves verifiable content. Answers are generated with citations and SQL-backed data, which forces the model to rely on evidence instead of inventing facts.

  • What makes Text‑to‑SQL reliable in production?

    Reliability comes from iterative tuning (SageMaker), experiment tracking (MLflow), schema validation, and execution safeguards (parameterized queries and sandboxed read‑only roles).

  • Can other clouds reproduce this?

    Yes. The pattern is vendor-neutral: a model-serving layer + agent coordination + vector search + ETL + safe query execution can be implemented on any major cloud or hybrid stack.

  • What are common pitfalls?

    Underestimating data quality, skipping Text‑to‑SQL testing, failing to attach provenance, and not planning for ongoing drift detection.

  • Who benefits most inside an organization?

    Brand owners, agencies, analysts, marketers, and media partners—anyone who needs fast, verifiable insights rather than polished but slow research deliverables.

Next steps

AI agents and grounded LLMs turn generative AI from a creative assistant into a practical analytics engine. For leaders, the question is simple: which recurring analysis would your teams kill for if it took 60 minutes instead of six weeks? Start by centralizing your best data, pilot a Text‑to‑SQL flow on a narrow schema, and treat model governance as a product requirement—not an afterthought.

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