Blits.ai
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Branding

Give your brand one consistent voice across every touchpoint. AI agents and digital humans that sound like you in every conversation.

Brand consistency
Creative AI
Customer experience

Where brand teams get stretched

The more channels you add, the more your brand drifts. These are the six spots we see most.

Brand consistency

Your voice, messaging, and visual identity drift a little further with every new channel and touchpoint.

  • One brand voice and tone
  • Consistent messaging across channels
  • A standardized visual identity

Content creation at scale

Every channel wants fresh, on brand content, and your creative team only has so many hours in a week.

  • Automated content generation
  • Brand aligned messaging
  • Multiformat content adaptation

Customer experience

Loyalty and advocacy come from experiences people remember, and generic scripts rarely deliver them.

  • Personalized brand interactions
  • Conversations that build emotional connection
  • Automated brand storytelling

Brand monitoring

Perception, sentiment, and mentions shift by the hour across platforms, and you need to see it in real time.

  • Real time brand sentiment analysis
  • Social media monitoring
  • Competitive brand analysis

Brand personalization

Customers expect experiences made for them, and personalization cannot come at the cost of brand integrity.

  • Tailored to each customer's preferences
  • Contextual brand messaging
  • Adapts to context, stays on brand

Creative resource constraints

Tight deadlines and lean creative teams make it hard to keep every brand experience consistent and high quality.

  • AI assistance for your creative team
  • Automated design generation
  • Faster creative workflows

AI use cases for brand and marketing teams, and how we build them on Blits.ai

Six use cases that brand and marketing teams already run in production: what each one does, how we build it on the Blits.ai platform, and what it can be worth for an organization of a given size.

Marketing content and compliance

AI marketing content and compliance copilot drafts campaign copy and checks it against advertising rules before a human approves it for publication. It writes campaign copy, product explainers and social posts on brand and in the customer's language from approved product facts, then runs a first pass compliance check against advertising rules and required disclosures, flagging unsupported claims and missing warnings before a human in marketing compliance approves publication.

Built withChatbots
USD 74,000 to USD 510,000
Staff time released plus reduced agency spend, per year. Reference: A retail bank producing 1,500 marketing assets a year across two languages.
How we build it on Blits.ai

On Blits.ai the copilot is two AI agents: a drafting agent and a separate compliance check agent, each with its own prompt versioning. Both draw on a knowledge base with the approved product facts, brand guide, disclosures and the compliance rule library, retrieved with hybrid search, and custom functions pull current rates and fees from the product system so numbers are inserted, not generated. Structured output returns the draft, its claims with sources and the compliance flags with the rule each relies on.

An agentic workflow routes drafts to the compliance reviewer with human in the loop approval, and run history with a full audit trail keeps every version and decision. Marketers work in Microsoft Teams or the web; machine translation and multi language support cover localization, and guardrails block banned claims before a draft reaches a reviewer. Test suites replay past approved and rejected assets after every change, and the platform is model agnostic, so drafting and checking can run on different models.

See the 5 public deployments

Marketing personalization at scale

AI marketing personalization at scale is AI that decides, for each customer, which product, offer, message or content to show next, for a marketing team running many campaigns. It works across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the team's engine across many campaigns and channels, not an agent that converses with the customer.

Built withChatbotsAgentic AI
USD 800,000 to USD 4.8 million
Incremental contribution from personalization, per year. Reference: A consumer business with USD 200 million a year in revenue from marketing driven campaigns.
How we build it on Blits.ai

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.

See the 9 public deployments

Conversational shopping assistant

An AI conversational shopping assistant answers product questions and recommends items from a retailer's own catalog, for a need, project or occasion a shopper describes. It works on the retailer's site or app, compares items grounded in the retailer's product data, reviews and stock, and hands the shopper to a basket, a store or a human expert.

Built withVoice BotsChatbotsAgentic AI
EUR 60,000 to EUR 720,000
Incremental gross margin from assisted sessions, per year. Reference: An online retailer with EUR 200 million in annual online sales.

Conversion uplift: median 100%, reported by 3 public deployments.

How we build it on Blits.ai

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.

See the 12 public deployments

Ad creative generation and testing

AI generated ad creative production and testing is generative AI that creates the images, video and copy variants a paid media campaign needs, for marketers. It scores or tests the variants against real performance data and lets a marketer pick, adjust and publish the ones that work, instead of a manual shoot and edit for every format and audience segment. This page covers making and testing the creative itself; choosing which offer or message to show which customer is covered separately under AI marketing personalization at scale.

Built withAI Platform
USD 600,000 to USD 2 million
Creative production cost avoided, per year. Reference: A consumer brand running USD 20 million a year in paid social and search media.
How we build it on Blits.ai

Blits.ai does not replace a dedicated ad creative platform, but an AI agent can run the brief and review workflow around one. The agent draws on a knowledge base of approved brand guidelines and substantiated product claims, uses the built in image generation tool to produce draft visuals and the built in image search tool, which searches stock photography, for reference and placeholder imagery, and calls the ad platform's own generative and publishing APIs through custom functions.

An agentic workflow with human in the loop approval holds every generated asset for a marketer to approve or reject against the brand guidelines before a custom function publishes it, and guardrails check generated copy against the same claims policy the knowledge base holds. Test suites can run a fixed set of briefs through the agent on every change to catch drift from brand guidelines before it reaches a live campaign, and the workflow's run history keeps a full audit trail of every run, including which generated assets a marketer approved or rejected. The platform is model agnostic, so a marketing team can change the underlying model without rebuilding the workflow.

See the 10 public deployments

Content localization

AI content localization translates and adapts an organization's commercial content for each market and language, so marketing and product teams can reach customers in their own language. It works across campaigns, emails, product pages, help content and websites, using the brand's glossary, style guide and past approved translations, then routes output to human linguists and local marketers for review in proportion to how visible and risky the content is.

Built withAgentic AI
USD 45,000 to USD 180,000
Localization cost avoided, per year. Reference: A consumer brand that localizes 3 million words of commercial content a year.
How we build it on Blits.ai

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.

See the 5 public deployments

Customer feedback analysis

AI customer feedback analysis is AI that reads every piece of free text customer feedback and turns it into themes, sentiment and actions for a named owner. The feedback can be survey verbatims, NPS comments, reviews, social posts, chat and call transcripts, and the analysis also identifies drivers, so the organization hears all of its customers instead of a sample.

Built withAI Platform
USD 72,000 to USD 378,000
Manual feedback coding cost avoided, per year. Reference: A consumer brand or public service that receives 300,000 free text feedback items a year.
How we build it on Blits.ai

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.

See the 11 public deployments
See all 24 AI use cases in marketing in our AI Use Case Library

How we deliver a use case: workshop, build and integration, deployment and support →

What matters when AI speaks for your brand

Every answer, post and offer an AI produces carries your name. These are the questions brand, marketing and legal teams ask, and how Blits.ai answers them.

Regulations these use cases are mapped to in our AI Use Case Library

EU AI ActGDPRUK GDPRFCA Consumer DutyEuropean Accessibility ActTelephone Consumer Protection ActISO/IEC 42001

One brand voice in every language

Set the persona, tone and terminology once, and every assistant and every draft uses them. A knowledge base holds your brand guide, glossary and past approved translations, so content in Arabic (including White Saudi, Bahraini, Egyptian, Jordanian and Libyan), English and every other language you serve reads as your brand. Test suites flag drift from the guide before a change goes live.

Only claims you can stand behind

An AI that invents a feature, a price or a promise does more harm than good. Assistants answer only from your approved product facts and brand guidelines, and refuse or hand over when the answer is not there. Guardrails block banned claims and check required disclosures, and custom functions insert current prices from your systems instead of letting a model write them.

The same brand at every touchpoint

Web chat, voice, WhatsApp, your app, kiosks and digital humans share one setup: the same knowledge, the same guardrails and the same persona. A digital human gives your brand a face and a voice that customers can talk to, and it gives the answer your chat assistant would give, in the same words.

Advertising rules and AI transparency

AI adds new rules on top of advertising law. The EU AI Act asks that people know when they talk to an AI, GDPR sets the terms for using customer data in personalization and the UK Consumer Duty expects clear communication. Blits.ai keeps every draft, approval and conversation in an audit trail, so you can show what went out and who approved it.

Customer data that stays where it must

Personalization runs on customer data. 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. PII masking removes personal data at the gateway before a model sees it, and the platform is ISO 27001, ISO 42001, SOC 2 Type II and PCI DSS Level 1 certified.

People approve what goes out

Generated copy, creative and translations wait in an agentic workflow with human approval steps until a marketer, linguist or compliance reviewer signs off. In conversations, complaints and questions outside approved content go to a person with the full conversation attached. Analytics on sentiment, satisfaction and intents show how customers respond to what you publish.

What brand and marketing teams achieve with AI

Across 159 public AI deployments in marketing at 154 organizations in 31 countries, these are the results marketing teams report.

43%

Conversion uplift

median across 11 public deployments

80%

Cost reduction

median across 5 public deployments

84%

Accuracy

median across 3 public deployments

Figures are reported by the organizations themselves or their technology vendors and checked in the Blits.ai AI Use Case Library, where each use case page lists the organizations and sources. Explore all 24 use cases in the AI Use Case Library.

Show us where your brand slips

First you see a working demo. A few weeks later it is live.

Book a demoExplore the platform

Frequently asked questions

The numbers behind the shift

Market context, with sources. The opportunity is real, and so is the risk of getting it wrong.

95%
of enterprise generative AI pilots deliver no measurable impact on the P&L, so execution and the right use case matter more than the model
Source: MIT report (via Fortune)
30%+
of generative AI projects were expected to be abandoned after proof of concept by the end of 2025, which is why a productized, tailored platform beats a bespoke build
Source: Gartner
two thirds
of all customer service chats handled by Klarna's AI assistant in its first month, doing the work of 700 agents
Source: Klarna

We help brand and marketing teams with