Bodham
bodham - understanding, the moment a thing becomes known
Ask the question in plain language; get validated SQL, charts, and an answer.
Bodham turns natural-language questions into data answers. Connect a database or upload a spreadsheet, then ask what you want to know. An agentic pipeline converts the question into validated SQL, runs it, and returns interactive charts, clean tables, and a written insight summary. Saved chats, pinned charts, and voice input make exploring data conversational, putting real analytics in the hands of business users rather than only the analysts who can write the query.
Workflow
Problem
Business questions queue behind the analysts who can write SQL, so decisions wait days for numbers that took minutes to produce.
Outcome
Analytics in the hands of any business user, with the query validated and the reasoning shown rather than hidden.
01
Connect the source
A governed database or an uploaded spreadsheet becomes the grounded scope for questions.
02
Convert and validate
The question is turned into SQL and validated before it runs, not after it returns something odd.
03
Return the answer
Results come back as interactive charts and tables, with the query visible for anyone who wants to check it.
04
Explain and keep
A written summary accompanies the numbers, and useful answers are pinned and saved.
Modules
Agentic Query Pipeline
A question converted into validated SQL, executed, and returned with the query shown.
Interactive Results
Charts and clean tables generated from the result, pinnable into a working set.
Insight Summaries
A written reading of what the numbers say, alongside the numbers themselves.
Voice & Saved Chats
Voice input and saved conversation history so analysis is resumable, not repeated.
Representative use cases
Ad Hoc Revenue & Margin Querying in Enterprise Sales Operations
A nationwide medical equipment distributor managing 45 regional sales territories, 12,000 hospital accounts, and over 150,000 quarterly order transactions in PostgreSQL databases.
Automated Spreadsheet Aggregation & Due Diligence for Financial Analysis
A mid market private equity firm evaluating manufacturing acquisitions, analyzing hundreds of unstandardized financial spreadsheets, historical general ledgers, and trial balance files.
Voice Enabled Floor Inventory & Dispatch Tracking in Freight Logistics
A third party logistics provider operating 8 regional freight cross dock distribution centers managing 65,000 pallet movements and 1,200 daily line haul truck arrivals.
Multi Table Customer Cohort Retention & Campaign Attribution in Ecommerce
A direct to consumer omnichannel apparel brand with 2.5 million customer profiles, 15 digital marketing channels, and complex multi touch purchase journey logs.
Telemetry Driven Proactive Churn Prevention in Enterprise B2B SaaS
A B2B enterprise software provider managing 850 corporate accounts, with application usage logs, feature adoption events, and support tickets stored in cloud data warehouses.
Live Conversational Boardroom Reporting & Real Time Strategic Analytics
The executive leadership team of a global retail group operating 350 department stores and an international ecommerce marketplace.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
Seconds
from question to charted answer
No SQL
required of the person asking
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
7 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
Covered by the platform
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
3 agent roles
Pack Architecture & Agent Composition Graph
10 Composed Agents- AGT-0100
Close Acceleration Agent
Finance
- AGT-0101
AP Invoice Processing Agent
Finance
- AGT-0102
AR Collections Agent
Finance
- AGT-0278
Natural Language to SQL Agent
Data and Analytics
- AGT-0279
Data Catalog Curation Agent
Data and Analytics
- AGT-0280
Data Quality Monitoring Agent
Data and Analytics
- AGT-0134
Spend Analysis Agent
Procurement
- AGT-0135
Supplier Discovery Agent
Procurement
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
Capability atoms
- nl-to-query
- system-connectors
- workflow-orchestration
- evidence-audit-trail
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- SAP S/4HANA
- Workday
- Oracle NetSuite
- Snowflake
- SAP MCP
- Dun and Bradstreet API
- dbt
- Monte Carlo
Designed for
technology, retail-cpg, financial-services, logistics
Business case
Model a Bodham scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 9% | — | — | — | — |
| Expected | 18% | — | — | — | — |
| Upside | 24% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending1 Agrani candidate in this solution: Sentiment Detection AgentPairs well with