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On Blits.ai this is an AI agent with a knowledge base that holds product guides and how to content, retrieved with hybrid search, plus SQL knowledge bases or custom functions that query the product catalog, live price and stock (REST calls or SQL queries). The agent asks clarifying questions, recommends and compares, and the chat widget shows the result as product recommendation, retail and order cards or a carousel; basket and reservation actions are custom functions with their own limits.

The same assistant runs on web chat, WhatsApp, voice and a mobile app (through the REST or WebSocket API channel), and can appear as a digital human, a photorealistic avatar streamed to the browser. Guardrails block prompt injection and apply the retailer's own policies on unsafe advice, PII masking protects personal data, and human handover routes complex projects to an expert. Test suites run category question sets on every change, analytics show satisfaction, response feedback and top intents, and the platform is model agnostic. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises, as needed.

On Blits.ai the decisioning usually stays in the organization's own next best action engine or customer data platform, which custom functions call through REST (the integration catalog includes Salesforce, Snowflake and Databricks). Blits.ai adds the conversational and content side: an agentic workflow in which an AI agent writes copy variants from a knowledge base of approved claims and brand guidelines, and guardrails with admin authored policies check each variant for prohibited claims and missing disclosures before human in the loop approval.

When a customer responds or asks about an offer, a customer facing agent presents it on web chat, WhatsApp, SMS or email, or inside the organization's own app through the REST or WebSocket API channel, with rich product recommendation cards; it explains the offer, answers questions and uses human handover when needed. Multi language bots serve each customer in their language, analytics and response feedback show how customers react, and test suites check the agent's output against your rules before every release. The platform is model agnostic. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.

On Blits.ai this is an AI agent with custom functions that call the retailer's order management, carrier tracking and returns APIs (REST calls with their own authentication and limits), and a knowledge base with the approved delivery, returns and warranty content, retrieved with hybrid search. Return eligibility and refund rules run as deterministic steps in a flow or inside the function, so the model explains the decision rather than making it; larger refunds can go through an agentic workflow with human in the loop approval above a set threshold.

The same agent serves web chat, WhatsApp, email and voice, and a mobile app through the REST or WebSocket API channel; the chat widget can show orders and receipts as rich cards. Guardrails check input and output, PII masking and card number tokenization happen at the gateway, and human handover escalates the conversation to the service team, including through Salesforce, Freshdesk or Zoho SalesIQ. Test suites run multi turn order and return conversations as regression tests after each change, and monitors check the order lookup against a test order on a schedule. The platform is model agnostic, so the model can be chosen or switched per agent.

On Blits.ai this is an agentic workflow triggered on a schedule or from the content system through an API token: an AI agent translates and adapts each item using a knowledge base that holds the glossary, style guides and past approved translations (retrieved with hybrid retrieval), and returns the result with structured output per segment. Custom functions read from and write back to the content management or ecommerce system through REST.

Human in the loop confirmation makes a linguist or local marketer approve or reject the write back of higher tier content before it is published, guardrails check terminology and block unapproved claims, and PII masking at the gateway removes personal data before text reaches a model. Test suites with LLM based grading score output against approved reference translations per language, including Arabic with regional models, and the platform is model agnostic, so each language can use the model that scores best.

The order scoring model itself is bought from a specialized fraud vendor and runs in the checkout pipeline, because it has to answer before the page moves on. Blits.ai builds the workflow around the decisions that need a person. An agentic workflow, triggered through the API when an order is held for review, pulls the retailer's own risk policy from a knowledge base, gathers the order, customer and chargeback history through custom functions with REST or SQL calls into the order management and payment systems, and drafts a recommendation with the evidence behind it as structured output.

Human in the loop approval holds any recommendation to decline, cancel or refund above a set value for an analyst, and the tool execution policy limits which systems the workflow may write to on its own. When a held order needs the customer's own input, such as confirming a changed shipping address, an agent reaches them on web chat, WhatsApp or email and passes the answer back into the workflow.

Guardrails and PII masking keep card and identity data out of free text prompts, every run keeps a full audit trail, and test suites run the workflow against a labeled set of past cases before any change to the policy goes live. The platform is model agnostic, so the risk team can choose the model per workflow.

Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.

On Blits.ai this runs as an agentic workflow on a schedule: custom functions pull new feedback from survey, review and ticketing systems through their REST APIs (the integration catalog includes Zendesk, Salesforce and ServiceNow) and strip names, account numbers and contact details before the data reaches the agent, and an AI agent with structured output tags each comment with themes from your codebook, sentiment per theme and any suggested action. The results are written to a SQL database registered as a SQL knowledge base, so an agent can answer plain language questions from analysts and theme owners over them.

For feedback that comes in through Blits.ai channels, PII masking at the gateway removes personal data from chat input, and the built in analytics already show satisfaction and sentiment (with engines from Amazon, Google, IBM and Microsoft), thumbs up and down feedback with comments on responses, and conversation level CSAT ratings.

Test suites with deterministic rules for label matching (or LLM based grading for summaries) let you run your labeled comments through the classifier after every prompt change, human in the loop approval holds routing of sensitive comments until a person confirms, and monitors run scheduled checks against the classifier agent and alert when it fails. The platform is model agnostic.

Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.