Summarization use cases#

aidb.summarize_text_aggregate() turns text summarization into a native SQL aggregate — the same GROUP BY semantics every analyst already knows. This page covers the business value and enterprise use cases.

The problem#

Enterprises store massive volumes of unstructured text inside PostgreSQL — customer feedback, support tickets, sales notes, compliance logs, clinical narratives. Extracting insight from this data traditionally requires a fragile pipeline: extract from the database, transform and batch it, send to an external LLM API, parse the response, then load results back. Every step introduces latency, failure modes, security exposure, and engineering cost.

Analysts who know SQL are locked out entirely — they need a data engineer and an ML engineer just to answer a question like “what are customers in the Northeast complaining about?”

What AIDB does differently#

AIDB brings the LLM to the data instead of moving the data to the LLM. Summarization becomes a native SQL aggregate:

SELECT region,
       aidb.summarize_text_aggregate(
           customer_feedback,
           aidb.summarize_text_config(gpt-4o)::json
       ) AS feedback_summary
FROM support_tickets
WHERE created_at > NOW() - INTERVAL 7 days
GROUP BY region;

One query. No pipeline. No Python. No external orchestration. Each row contains an LLM-generated summary of all feedback for that region — returned directly to the SQL client.

The same data, a different question, a different prompt:

SELECT region,
       aidb.summarize_text_aggregate(
           customer_feedback,
           aidb.summarize_text_config(
               gpt-4o,
               prompt => Identify the top 3 product issues and suggest fixes
           )::json
       ) AS issues
FROM support_tickets
WHERE created_at > NOW() - INTERVAL 7 days
GROUP BY region;

Same function. One string changed. Entirely new analysis — no code deployed, no pipeline modified.

Business value#

Data never leaves the database — In regulated industries (healthcare, finance, government), moving sensitive data to an external API can require months of compliance review. AIDB keeps all data inside PostgreSQL. The LLM endpoint can be on-premise, in a private VPC, or running locally.

SQL is the universal interface — There are roughly 10 million SQL-literate professionals worldwide. AIDB turns every analyst, DBA, and BI developer into an AI-powered analyst without new tools, notebooks, or API keys to manage.

Eliminates an entire category of infrastructure — No Airflow DAGs, Lambda functions, SQS queues, or Kubernetes jobs. The database is the compute layer.

Real-time, ad-hoc intelligence — Traditional NLP pipelines are batch-oriented. AIDB enables ad-hoc queries against live data. An executive can ask a new question at 3 PM and have the answer by 3:01 PM.

Scales with PostgreSQL — Leverages PostgreSQL’s mature query planner, parallel workers, connection pooling, and role-based access control. Enterprises already invested in PostgreSQL infrastructure get AI capabilities at zero marginal infrastructure cost.

Enterprise use cases#

Retail and e-commerce — GROUP BY product category across millions of reviews and feedback entries. Get per-category summaries of preferences, complaints, and emerging trends — updated in real time as new reviews arrive.

Financial services — GROUP BY risk segment across loan officer notes, borrower communications, and underwriting comments. Surface patterns that quantitative models miss — narrative signals in free-text fields that precede defaults.

Healthcare and life sciences — GROUP BY facility, department, or condition across patient feedback, clinical notes, and discharge summaries. Identify care quality variations without exposing PHI to external services.

Legal and compliance — GROUP BY jurisdiction or regulation type across thousands of compliance documents, audit findings, and regulatory correspondence. Reduce weeks of manual review to a single query.

Sales and customer success — GROUP BY sales rep, region, or deal stage across CRM notes, call transcripts, and email summaries. Extract win/loss patterns and coaching opportunities from the unstructured text in every CRM.

Manufacturing and supply chain — GROUP BY supplier or component across procurement correspondence, quality reports, and shipment notes. Detect supply chain risks from narrative signals before they show up in delivery metrics.

HR and talent — GROUP BY department, tenure band, or role level across exit interview transcripts and employee survey responses. Surface the real reasons people leave.

How it works#

AIDB implements a true PostgreSQL aggregate using the database’s native state-accumulation framework:

State function — accumulates text from each row in a group, maintaining per-group state in memory
Finalize function — sends the accumulated text to the configured LLM and returns the result
Chunking engine — automatically splits large text groups into manageable chunks (configurable by word count or character count, with overlap for context preservation)
Reduction strategies — append concatenates chunk summaries; reduce iteratively re-summarizes until the output converges — a true map-reduce pattern running inside the database
Custom prompts — each query can specify its own instruction: summarize, translate, extract sentiment, categorize, identify trends — making the same function infinitely flexible
Model-agnostic — works with any OpenAI-compatible endpoint (GPT-4o, Llama, Mistral, on-prem models) configurable per query

The wow moment#

Start with a table of customer preference data — 126 rows across 20 customers and 5 product categories. The raw data is a wall of unstructured text.

Run this query:

SELECT name,
       aidb.summarize_text_aggregate(
           preferences,
           aidb.summarize_text_config(gpt-4o)::json
       ) AS style_profile
FROM customer_preferences
GROUP BY name;

In seconds, 126 rows become 20 rows — each a rich, coherent style profile synthesized from every data point for that customer. No pipeline. No engineering team. No data left the database.

Now change the prompt:

SELECT category,
       aidb.summarize_text_aggregate(
           preferences,
           aidb.summarize_text_config(
               gpt-4o,
               prompt => Identify the 3 strongest purchasing signals and recommend inventory actions
           )::json
       ) AS buying_signals
FROM customer_preferences
GROUP BY category;

Same data. Same function. Different question. Different insight. The analyst changed one string and got an entirely new analysis — no code deployed, no pipeline modified, no ticket filed.

That is the moment it clicks: the entire organization’s unstructured data becomes queryable with natural language, using the tool analysts already know.

Example with chunking and custom prompt#

For large text groups, combine chunking with a targeted prompt:

SELECT product_category,
       aidb.summarize_text_aggregate(
           review_text,
           aidb.summarize_text_config(
               local-llama,
               aidb.chunk_text_config(120, 120, 15, words),
               Identify the top 3 quality issues and suggest improvements,
               reduce
           )::json
       ) AS quality_analysis
FROM product_reviews
GROUP BY product_category;

Competitive position#

Capability	Traditional ETL + LLM API	AIDB in-database aggregate
Data movement	Required (security risk)	None (data stays in place)
User interface	Python / custom app	SQL (universal)
Infrastructure	Airflow + queues + compute	PostgreSQL (existing)
Time to first insight	Days to weeks	Minutes
Ad-hoc queries	Requires new pipeline	Change the SQL
Access control	Separate system	PostgreSQL RBAC / RLS
Audit trail	Must be built	PostgreSQL native logging
Compliance	Requires separate review	Inherits DB compliance posture
Model flexibility	Locked to provider	Any OpenAI-compatible endpoint

Summarization use cases

Contents