Entity extraction
Turns notes and calls into structured properties: budget, decision-maker, urgency and competitor.
Data Hygiene & Operations Agent
Data Agent
Silent, continuous management of your CRM. Extract entities, update deal stages and keep your database clean without friction or manual work.
Intelligence Engine
Technology Stack
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Google Gemini
ClaudeData hygiene running in the background
Diagnosis
From an outdated CRM to reliable data
We solve the structural cause of dirty data before building reports or predictive scoring.
Current situation / Problem
- Outdated CRM caused by reps not manually filling in fields.
- Inaccurate sales forecasts and duplicate data.
- Administrative tasks that eat into selling time.
Awtana Solution
- Automatic extraction of structured data from notes or calls.
- Automatic correction of amounts, dates and enrichment of customer records.
- Background agent executing real-time updates.
Roadmap
Deployment work phases
A gradual, auditable rollout, with verifiable criteria at the close of each phase.
- Phase 00
Data Audit & Business Rules
Diagnosis of database quality, critical properties, duplicates and definition of validation rules.
Data dictionary and rules approved.
- Phase 01
Extraction Algorithm Configuration
Modeling of entity extraction from notes, emails and call transcripts.
Extraction accuracy validated on a sample.
- Phase 02
Bidirectional CRM Connection
Secure sync with your CRM to read context and write properties, stages and amounts.
Agent writing to production with full traceability.
- Phase 03
Database Hygiene Monitoring
Dashboard of completeness, detected duplicates and applied corrections, with periodic review.
Sustained increase in critical field completeness.
Capabilities
What the agent executes on your CRM
Continuous hygiene and enrichment operations, with a log of every change applied.
Stage updates
Moves deals between stages based on real conversation signals and pipeline rules.
Deduplication and enrichment
Detects duplicate records, normalizes formats and completes company and contact records.
Reliable forecasting
Amounts and close dates corrected so forecasts reflect the real pipeline.
Investment model: Setup Fee (Deployment) + Monthly Retainer (Maintenance, tokens and monitoring).
RESULTS & CLIENTS
- 10+
- Years of experience
- 300+
- Projects delivered
- 98%
- Customer satisfaction
Well Supported
“Pleasant, assertive, and with great willingness and flexibility to achieve objectives.”
Verified HubSpot Partner Review
FAQ
Frequently asked questions
Direct answers to the most common questions from sales and technology teams.
What does the Data Optimization Agent do in the CRM?
It extracts structured information from notes, emails, and calls, updates stages and amounts, and continuously cleans up duplicate records.
- Extraction: budget, decision-maker, urgency, and competitor.
- Updates: deal stage, close date, and amount.
- Hygiene: deduplication, format standardization, and enrichment.
Is information shared with the agent used to train models?
No. Operations are governed by zero-retention agreements: data sent to the models is neither stored nor used for training.
- Enterprise APIs with contractual zero data retention.
- Data isolation by customer, with no shared context between instances.
- Encryption in transit and credentials managed in a secrets vault.
- Option to mask sensitive fields before sending them to the model.
Does the agent use RAG or fine-tuning to understand the business?
It uses RAG with the company's own knowledge base, allowing information to be updated without retraining the model.
- RAG: retrieves documents, policies, and catalogs before responding.
- Immediate updates when a source document is uploaded or modified.
- Fewer hallucinations: every response is grounded in verifiable content.
- Fine-tuning is reserved for style or format adjustments.
Can the agent modify data without supervision?
Not indiscriminately: the company defines which fields it can write to automatically and which require human approval.
- Allowlist of properties the agent can write to.
- Amount or stage changes above a defined threshold are sent for review.
- Audit history with the previous value and the new value.
Which CRMs and data sources does it work with?
It works natively with HubSpot and connects via API to Salesforce, ERP systems, and call transcription tools.
- HubSpot: reading and writing contacts, companies, and deals.
- Sources: emails, notes, Aircall transcripts, and meetings.
- Output: structured properties ready for reporting.
How does it improve sales forecast accuracy?
It corrects amounts and close dates based on actual evidence from the conversation, eliminating the bias of manual data entry.
- Detection of stalled deals with no recent activity.
- Alerts for overdue or unrealistic close dates.
- Completeness of critical fields monitored on a dashboard.
How long does implementation take?
Between 4 and 6 weeks, starting with the data audit and property dictionary.
- Weeks 1-2: quality audit and validation rules.
- Weeks 3-4: extraction algorithms and testing on a sample.
- Weeks 5-6: bidirectional connection and production monitoring.
What is the investment model and expected return?
A deployment Setup Fee plus a monthly retainer; the return is seen in administrative hours freed up and pipeline reliability.
- Less time spent by salespeople on manual data entry.
- Reduction in duplicates and empty critical fields.
- Forecast with measurable variance against actual closed results.