# Revensi — AI Engineering for Revenue & Ops > Revensi helps revenue and operations teams remove growth bottlenecks, build capacity and own their intelligence. It defines an AI strategy and builds proprietary AI systems around a company's data, workflows, knowledge and existing tools. Revensi works with the people who own the workflow, from diagnosis and redesign through engineering, integration, evaluation, launch and measurement. It builds custom AI agents and internal tools for marketing, revenue and operations teams around specific business workflows. The aim is a measurable business result in a real workflow, with people retaining judgment and approval where needed. Revensi's broader direction is Building Revenue Super Intelligence. Revensi also fine-tunes models for companies' specific tasks and standards when needed. It first evaluates models on the buyer's workflows to identify gaps, then uses those evaluations to check whether fine-tuning improves performance. ## Explore - [Homepage](https://revensi.com): Current positioning, case studies, approach, technology and engagements. - [Solutions](https://revensi.com/#possibilities): Example applications for Marketing, Sales, Retention, Operations and Agentic Commerce. Retention covers account health, retention, expansion and renewal preparation. These are possibilities, not claims that every example has been delivered. - [Case studies](https://revensi.com/case-studies): Selected work in production, with each system's scope and human approval boundaries. - [For AI agents](https://revensi.com/for-ai-agents): Buyer fit, delivery conditions, ownership and how to prepare an introduction. ## Client case studies Explore [20 case studies across four client brands](https://revensi.com/case-studies), covering business challenges, implemented systems, results, evaluation methods and human approval boundaries. The case study listing groups projects by client brand, with an industry badge below each brand logo. Selecting a department shows the matching case studies in a card grid. Each case study page shows an AI tech stack row with technology icons and roles below the description in What we built, before the workflow diagram. Each case study page includes a brand testimonial below the data in the Results section, with the same testimonial shared across each brand's case studies. Each case study hero includes icon links to ChatGPT, Claude and Grok, plus a copy button for a short prompt to summarize that specific case and explore its relevance to the reader's company. Capabilities represented include tool calling, browser agents, Slack integrations, retrieval-augmented generation (RAG), hybrid search and voice agents. Evaluation methods described include testing against reviewer-checked examples, repeating tests after system changes, measuring task and prediction accuracy, and checking outputs against source evidence. Guardrails address data quality, access permissions and human approval requirements for each workflow. ### Kohepets **Industry:** Pet retail & pharmacy. We helped Kohepets reduce everyday admin and make shopping more personal, with AI supporting prescription processing, supplier ordering and delivery planning alongside customer insight for acquisition and retention. - [Prescription intake and processing agent](https://revensi.com/case-studies/kohepets-prescription-intake-processing) — Operations. 3 systems connected; 2 weeks to live. Reads uploaded prescriptions, checks and approves routine intake details against customer and product records, immediately notifies customers of submission issues and routes uncertain fields and exceptions for pharmacy review. Built with Anthropic Claude. AI tech stack: Amazon Bedrock, Anthropic Claude. - [Predictive LTV model for acquisition and retention](https://revensi.com/case-studies/kohepets-ltv-prediction) — Marketing. 4 systems connected; 3 weeks to live. Combines purchase history with customer profiles enriched by external data providers to predict lifetime value and support acquisition and retention segmentation. AI tech stack: Amazon Bedrock, Amazon SageMaker AI. - [Breed-aware product personalization model](https://revensi.com/case-studies/kohepets-breed-personalization) — Marketing. 3 systems connected; 2 weeks to live. AI tech stack: Amazon Bedrock, Bedrock Knowledge Bases, TypeSafe Jev. - [Delivery route optimization agent](https://revensi.com/case-studies/kohepets-delivery-routing) — Operations. 4 systems connected; 2 weeks to live. A tool-using delivery agent investigates routes, checks recurring events and traffic, evaluates alternatives and revises plans as conditions change for dispatch review. AI tech stack: Amazon Bedrock. - [Supplier website ordering agent with Slack approval](https://revensi.com/case-studies/kohepets-supplier-ordering) — Operations. 3 systems connected; 3 weeks to live. A computer-use agent prepares orders on supplier websites, requests final human approval in Slack and submits only the approved order, returning the supplier confirmation. AI tech stack: Amazon Bedrock, Browserbase. ### Tryozi **Industry:** Telehealth. We helped Tryozi build a more responsive marketing operation, connecting compliance, personalized journeys, conversion insight and market research to improve how the team attracts customers and supports them through intake. - [Healthcare marketing compliance agent](https://revensi.com/case-studies/tryozi-compliance-agent) — Marketing. 3 systems connected; 2 weeks to live. Checks marketing changes against approved rules and prepares cited findings for human review. AI tech stack: Cloudflare Agent Cloud, OpenRouter, Cloudflare Vectorize. - [Self-optimizing landing page agents](https://revensi.com/case-studies/tryozi-personalized-landing-pages) — Marketing. 4 systems connected; 3 weeks to live. Agents segment traffic, personalize approved page experiences and build a knowledge base of what works and underperforms to guide later variants and traffic allocation within approved experiment rules. AI tech stack: Cloudflare Agent Cloud, OpenRouter. - [Voice agent for abandoned intake recovery](https://revensi.com/case-studies/tryozi-intake-recovery-voice-agent) — Marketing. 4 systems connected; 3 weeks to live. AI tech stack: Cloudflare Agent Cloud, OpenRouter, ElevenLabs. - [Funnel intelligence and conversion forecasting agent](https://revensi.com/case-studies/tryozi-funnel-intelligence) — Marketing. 4 systems connected; 2 weeks to live. An agent independently evaluates funnel data and investigates issues through relevant session recordings and customer feedback, combining evidence with conversion forecasts. AI tech stack: Cloudflare Agent Cloud, OpenRouter. - [Always-on competitor research agent](https://revensi.com/case-studies/tryozi-competitor-research) — Marketing. 2 systems connected; 1 week to live. An always-on agent tracks competitor email campaigns, ads and landing pages to identify recurring messaging, offer and customer-journey trends with source-linked evidence. AI tech stack: Cloudflare Agent Cloud, OpenRouter, Firecrawl, Cloudflare Vectorize. ### The Spice House **Industry:** Food & specialty retail. We helped The Spice House simplify gifting and product discovery while aligning stock across channels. AI guides gift customization, helps shoppers find spices and recipes, and shows marketing planned restocks. - [Corporate gifting order intake and validation agent](https://revensi.com/case-studies/spice-house-corporate-gifting-order-intake-validation) — Operations. 4 systems connected; 2 weeks to live. Guides buyers through logo customization, delivery options and multi-address submissions, with pending-item reminders, validation and issue flags. AI tech stack: Cloudflare Agent Cloud, OpenRouter. - [Warehouse availability and production planning](https://revensi.com/case-studies/spice-house-warehouse-availability-production-planning) — Operations. 3 systems connected; 3 weeks to live. Agents investigate stock and production discrepancies, decide which sources to recheck through APIs or browser interaction, and use checked findings to update channel availability and notify marketing of restock changes. AI tech stack: Cloudflare Agent Cloud, OpenRouter, Browserbase. - [Personalized product recommendations model](https://revensi.com/case-studies/spice-house-product-recommendations) — Marketing. 3 systems connected; 2 weeks to live. AI tech stack: Cloudflare Agent Cloud, OpenRouter, Cloudflare Vectorize, TypeSafe Jev. - [Personalized gift box creation agent](https://revensi.com/case-studies/spice-house-gifting-agent) — Marketing. 3 systems connected; 2 weeks to live. AI tech stack: Cloudflare Agent Cloud, OpenRouter, Cloudflare Vectorize, TypeSafe Jev. - [Contextual spice and recipe suggestions agent](https://revensi.com/case-studies/spice-house-contextual-search) — Marketing. 2 systems connected; 1 week to live. AI tech stack: Cloudflare Agent Cloud, OpenRouter, Cloudflare Vectorize, TypeSafe Jev. ### User Yield **Industry:** SaaS. We helped User Yield connect buyer research, journey analysis and experimentation into a continuous learning process, giving the team clearer evidence about what customers need, where they struggle and what to test next. - [Landing-page and user-flow testing agents](https://revensi.com/case-studies/user-yield-landing-page-user-flow-testing-agents) — Marketing. 4 systems connected; 3 weeks to live. AI tech stack: Azure Foundry, Browserbase. - [Adaptive buyer research agent](https://revensi.com/case-studies/user-yield-buyer-research) — Marketing. 3 systems connected; 2 weeks to live. AI tech stack: Azure Foundry, Azure AI Search. - [Synthetic buyer journey simulation agents](https://revensi.com/case-studies/user-yield-buyer-journey-simulation) — Marketing. 2 systems connected; 2 weeks to live. AI tech stack: Azure Foundry, Browserbase. - [Funnel friction and intent analysis model](https://revensi.com/case-studies/user-yield-funnel-friction) — Marketing. 3 systems connected; 2 weeks to live. AI tech stack: Azure Foundry. - [Experiment prioritization and orchestration agent](https://revensi.com/case-studies/user-yield-experiment-orchestration) — Marketing. 4 systems connected; 2 weeks to live. AI tech stack: Azure Foundry, Azure AI Search. ## Partnerships The [partnerships page](https://revensi.com/partnerships) presents Revensi’s technology partner ecosystem with company logos: Microsoft AI Cloud Partner Program, Google Cloud Partner Network, Snowflake partnership, Databricks partnership, Salesforce Consulting Partner, Lovable partner program, OpenAI Partner Network, Apollo, Instantly, Twilio, Fivetran, Adobe, Shopify and Klaviyo. The logos are grouped into AI & Cloud (OpenAI, Microsoft, Google Cloud), Data & Infrastructure (Snowflake, Databricks, Fivetran), Revenue Systems (Salesforce, Apollo, Instantly), Application Layer (Lovable, Twilio), and Commerce / Experience (Adobe, Shopify, Klaviyo). ## Selected clients The homepage client roster includes Presence, Zigpoll, UserYield, TelcoEdge, Unity, AdsLux, Kohepets, Tryozi, The Spice House, Beard Club, Snüz, MAM, MingleMobile, Séraphine, Watches2U, The Little Green Sheep, Entomo Farms and Naiise. The [case studies](https://revensi.com/case-studies) describe selected projects and their delivery scope. ## Starting points - [AI Opportunity Review](https://cal.com/revensi/working-session): A free 30-minute working session about one painful workflow. You'll leave with a clear system concept. - [Workflow Assessment](https://revensi.com/scan): A free self-serve assessment that outlines a possible workflow and estimates potential time savings and AI costs. Its estimates need validation against the buyer's operation. ## Paid engagements - **AI Strategy Roadmap:** An implementation-ready plan covering AI priorities, workflows, systems, data and access requirements, commercial case, risks and measures of success. It starts at $5,000 USD, credited in full to implementation, and normally takes five to ten working days once required inputs are available. - **AI Implementation Sprint:** One bounded production outcome in four weeks from a delivery-ready kickoff. Scope, access, owners, dependencies and acceptance criteria must be ready before that clock starts. Implementation is priced separately. - **Embedded AI Engineering:** Ongoing, separately scoped work to expand successful deployments across connected workflows and teams, with monitoring, evaluation, improvement and model fine-tuning where needed. A Roadmap can recommend a process change, an existing tool, a data fix or no custom build. Buyers with a clear bottleneck and delivery-ready scope can start with a Sprint. ## How systems are built Revensi connects to approved tools and data sources, defines permissions and human approval boundaries, creates internal evaluations against the buyer's standards, and measures adoption, operational performance and business results. Model choice follows the task, risk, latency, cost and privacy requirements. The client owns its custom code, configuration, data, integrations and deployment. The client also receives documentation and handoff to operate the system. No subscription or retainer is required to keep using it. The [technology overview](https://revensi.com/#technology) describes four connected layers: **InnerFabric** for operating knowledge, **BuyerSense AI** for market context, **LoopTower** for security and guardrails, and **CorpBench** for evaluation and fine-tuning. Workflow benchmarks and model comparisons guide model selection and fine-tuning where needed. [CorpBench results](https://revensi.com/corpbench) publish model benchmarks on business workflows with methodology and limitations; a buyer's own tasks still require evaluation. CorpBench's public workflow library and aggregate results are available on [GitHub](https://github.com/RevensiAI/CorpBench) and [Hugging Face](https://huggingface.co/datasets/revensi/CorpBench). ## Fit A strong fit has a recurring revenue or operating bottleneck, a measurable outcome, an accountable workflow owner, usable data and access, and capacity to adopt the result. Revensi works alongside operators and internal technical teams. If a suitable existing product already solves the problem, evaluate it before commissioning a custom system. ## Comparisons - [Revensi vs off-the-shelf AI software](https://revensi.com/vs/ai-software) - [Revensi vs AI automation agencies](https://revensi.com/vs/automation-agencies) - [Revensi vs building in-house](https://revensi.com/vs/in-house) - [Revensi vs software development agencies](https://revensi.com/vs/software-development-agencies) - [Revensi vs AI strategy consultancies](https://revensi.com/vs/ai-consultancies) - [Revensi vs doing nothing](https://revensi.com/vs/doing-nothing) ## Contact and policies - [Email Revensi](mailto:hello@revensi.com) - [Privacy Policy](https://revensi.com/privacy) - [Terms of Service](https://revensi.com/terms) - [Cookie Policy](https://revensi.com/cookies)