We solve
Too much copy-paste, slow quoting and analysis
Product errors and decisions without context
Opportunities and risks detected too late
AI Quoting vs. Traditional CPQ vs. Manual Quoting
AI Sales 2.0 combines AI assistance with ERP/CRM data, business rules and management approval.
| Task | Manual | Traditional CPQ | AI Sales 2.0 |
|---|---|---|---|
| Product discovery | Manual searches across catalogs and systems. | Filters and predefined configurations. | Hybrid AI + exact search by intent, SKU or specifications. |
| Complex RFQs | Sales interprets requirements manually. | Requirements are mapped to predefined fields. | AI interprets natural-language requirements and proposes products. |
| ERP & CRM data | Stock, prices and history checked separately. | Connected through predefined integrations. | Stock, pricing and customer context in one workflow. |
| Recommendations | Depend on salesperson experience. | Based mainly on predefined rules. | AI suggests substitutes, upsell and complementary products. |
| Business control | Rules checked manually. | Rules drive configuration and approvals. | Rules stay authoritative; AI assists, humans approve. |
| Quote preparation | Information gathered and copied manually. | Configured quotes are generated automatically. | Search, ERP data and recommendations in one flow. 33 > 7 min in case study. |
What AI Sales 2.0 delivers
Text, document, image and voice processing
Semantic and hybrid search
Explainable commercial recommendations
Synchronized ERP and CRM data
Quoting dashboard and commercial workflows
Forecasts, alerts and conversational analytics
Proven results: industrial implementation
Who uses the platform:
B2B Distributors and Importers
Sales and Management Teams
Retail, eCommerce and Technical Support
References
Phases of Setup
Data & Analysis: pilot objective, commercial workflow, catalog, customers, inventory and permissions
ERP and CRM integration: API, ODBC, CDC, CSV or RPA, plus product, price, unit, inventory and customer mappings
AI Pilot (2–4 weeks): search, quoting, recommendations, forecasting or analytics for the priority commercial process
User testing (30 days): quoting, adoption, upsell, recommendation quality and alert relevance KPIs
Stable implementation: roles, audit, workflow integration, continuous optimization and controlled expansion
See the architecture and value flow
Most companies have commercial data split across ERP, CRM, ecommerce, documents and files. AI Sales 2.0 connects as an intelligent layer over existing systems: it synchronizes and validates data, turns it into commercial context, and delivers quoting, recommendations, forecasts, alerts and analytics. Approved actions can be returned to ERP or CRM workflows.
Methodologies: OCR, multimodal input, semantic and lexical search, RAG, business rules, upsell and cross-sell recommendations, inventory forecasting and alerts, opportunity scoring, feedback loops, A/B testing, audit and integration through API, SQL, CSV or RPA
ERP platforms: Romanian and international
Romanian:
The platform is a smart quoting extension for Senior, Charisma, Entersoft, Winmentor, Nexus, Magister, Pluriva. We import the catalogue, offers and stocks in (almost) real-time, without modifying them.
International:
Microsoft D365, SAP Business One, etc.
We integrate any ERP via API, ODBC, RPA or scheduled imports (CSV, CDC, flat files etc).
ANIS Award-Winning AI 2026 Technical Guides
The OPTI “AI Architecture for B2B in 2026” program, which provides the foundation for AI Sales 2.0, received the ANIS R&D Program of the Year award. Read the guides for applied problems, architectures and technical solutions. Read the published AI for Sales Guide #1
Recommendations, upsell, and rules
KPI: +20% increased AI-assisted sales.
Integrating exact data with AI (RAG)
KPI: 94% hallucination evitation in RAG.
Input processing: text, voice, photo
KPI: <5 sec transcribing 50 products from image.
Hybrid search & assistants (chatbots)
KPI: -90% no-result searches.
Continuous optimization and learning
KPI: 2.5x scaling speed.
Security, audit, and standards
KPI: 0 detected deviations from company policy.
AI Sales 2.0 Packages
Internal Sales
- Quote generation in 5 minutes
- ERP integration
- Semantic and exact search
- Upsell and cross-sell rules
- Agent dashboard and customer history
- Warranty and technical support
Ecommerce Starter
- All Internal Sales features
- Ecommerce integration: Woo/Magento/Custom
- Public product and search chatbot
- Multiple recommendation rules
- Sales and catalog analytics
- SSO or login integration
Omni-Channel Sales
- All Ecommerce Starter features
- CRM integration: HubSpot/Dynamics 365/Salesforce
- Management dashboards
- Inventory and opportunity alerts
- Conversational analytics
- Continuous optimization and international ERP integration
Steps Overview
Step 1: ERP/CRM Analysis and Integration
We start with the commercial objective and the available data: products, prices, inventory, customers, orders, quotes and user roles. We establish a secure connection through APIs, ODBC, CDC, scheduled imports or other suitable methods, and define the indicators used to evaluate the pilot.
Step 2: AI Pilot for the Priority Process
We configure the capabilities required for the selected problem: semantic and exact search, document processing, assisted quoting, commercial recommendations, inventory forecasts and alerts, or conversational analytics. The pilot runs on examples and data relevant to the company.
Step 3: Dashboard, Workflow and Rule Configuration
We adapt the dashboards to the relevant user roles and configure business rules for pricing, upsell and cross-sell recommendations, margins, limits, approvals, alerts and data access. The platform is integrated into existing commercial workflows without replacing the ERP.
Step 4: 30-Day Testing and User Training
We train users and test the platform in real operations. We measure the agreed indicators, such as quoting time, product lookup errors, adoption, recommendation value, upsell opportunities or alert relevance.
Step 5: Go-Live and Continuous Optimization
After the pilot is validated, we extend the platform to the approved users and workflows. We provide technical support, auditing, monitoring and continuous optimization based on results, user feedback and changes in commercial data.
Frequent questions
How long does it take to see results?
Typically, a PoC is delivered in 2–4 weeks; full projects in ~7 weeks according to standard implementations.
If my ERP doesn't have an API?
We can start data ingestion via CSV/Excel or ODBC to connect to SQL databases. We use automation technologies (e.g., UiPath) when these options do not work. Data can definitely be extracted!
Can the data remain in the EU?
Data can be hosted in EU regions (e.g., Belgium/Netherlands/Germany/Poland) or in EU multi-region. For AI services, we select EU endpoints/regions where available; if a service does not yet offer "EU-only", we inform in advance and propose alternatives.
How do we avoid item errors and product code retrieval issues?
We use semantic (vector) search to understand the customer's intent, not just the exact code match. Thus, even if the customer does not remember the correct code, Gemini can suggest the appropriate items based on the description or context. In addition, we perform a parallel exact search for cases when the code is correct.
Can the public chatbot give wrong prices?
A full implementation based on RAG (Retrieval-Augmented Generation) technology reduces hallucinations by 50% and can bring error rates down to 4-5%. Additionally, we have special safeguards to ensure prices and stock levels are retrieved from standard relational databases.
Do you have specific industries where the solution is applied?
Yes.
1. Construction Materials & DIY:
There are thousands of similar items and different units of measure (sqm vs box). Plus, tender requests consisting of hundreds of pages are received.
2. Auto Parts & Service:
Part codes are complex, compatibilities are strict, and there is a very high volume of products.
3. The Book Industry:
There are tens or hundreds of thousands of titles requiring advanced search and intelligent recommendations, including dialogue with the highly rich content.
