ODI TV / AI & SOFTWARE SERVICES
We build custom AI chatbots, connect your business tools and turn repetitive work into practical software — for customer support, sales and day-to-day operations.
Start with one useful workflow. Build from a tested pilot.
PRACTICAL SOFTWARE. SPECIFIC OUTCOMES.
Choose a focused service. We define the deliverable, connect the right tools and test it against your workflow.
Help customers and teammates find answers in approved documents, product information and FAQs.
A focused assistant with source references and tested questions.
Connect intake, ticket routing, response preparation and case histories around your support process.
A support-workflow pilot with agreed handoffs and connected tools.
Move information between systems, validate records and handle repetitive administrative tasks.
A connected workflow with logs, retries and acceptance tests.
Clean spreadsheets, remove duplicates and build reports around the numbers your team needs.
Cleaned data, a change summary and a reusable reporting workflow.
Guide visitors through your services, gather project requirements and connect the next step to your sales process.
A scoped discovery assistant and an agreed booking or CRM integration.
Create focused internal apps, resolve reproducible bugs and test essential business workflows.
Working software, source code, test findings and handoff guidance.
Custom development services. AI models, system access, hosting and ongoing support are scoped for each project.
HOW A CUSTOM AI ASSISTANT WORKS
We bring AI models, your business knowledge and your tools together in a workflow built for a specific job. Here is the approach we scope and test for each project.
Identify what the customer or teammate needs and ask for any missing details.
Example: ask for an order number before checking delivery.Connect approved FAQs, documents or business records so the assistant has relevant context.
Example: find the matching order and delivery estimate.Prepare a useful reply or perform an agreed task through a tested integration.
Example: explain the status and prepare a support summary.Test answers and actions against real use cases, document gaps and refine the workflow.
Example: route an unknown order to your support team.A customer question. A new inquiry. A spreadsheet that needs cleaning. We define the result, build a focused pilot and test it before a wider rollout.
Tell us what you want to automateLESS EXPLAINING. MORE EXPLORING.
Change the rules. Run a workflow. Explore the numbers. These interactive prototypes show the kinds of tools we can tailor to your business.
Configure a branching workflow, run five fictional records and inspect the results. Try a destination failure or change the priority threshold.
To retry: switch off the failure option, then run again. Each run starts fresh and never creates a real task.
[
{
"id": "REQ-101",
"type": "Sales",
"email": "buyer@example.com",
"value": 2400
},
{
"id": "REQ-102",
"type": "Support",
"email": "help@example.com",
"value": 0
},
{
"id": "REQ-101",
"type": "Sales",
"email": "buyer@example.com",
"value": 2400
},
{
"id": "REQ-103",
"type": "Sales",
"email": "",
"value": 600
},
{
"id": "REQ-104",
"type": "Sales",
"email": "studio@example.com",
"value": 750
}
]What this demonstrates: branching rules, field validation, duplicate handling and inspectable results. No live CRM, API or AI connection.
Find a ticket, assign a team, change its priority and prepare an editable response. Status changes also update the analytics demo.
Template-based draft, not live AI. Nothing is sent.
Filter 24 fictional tickets, adjust the review-age target and inspect the underlying records. Your support-demo changes carry into these totals.
Targets are visitor-selected sample thresholds. These are not ODI client results, response-time guarantees or predictive metrics.
Inspect a sample case, update the evidence checklist and record a review step. Try marking it ready while evidence is missing.
Order DEMO-024 · $48.00 · one household item. This is a fictional order record.
Demonstrates evidence organization and review requirements. It does not detect fraud or approve or reject claims. The timeline resets with this demo; it is not a production audit log.
Paste CSV or choose a small sample file. Trim extra spaces, normalize an Email column and remove exact duplicate rows.
| Name | Company | |
|---|---|---|
| Avery | avery@example.com | North Studio |
| Jordan | jordan@example.com | Field Works |
| Morgan | Missing | Studio West |
Preview shows the first 8 rows. Blank fields stay blank; no missing information is invented.
Local processing only: your file is not uploaded or saved. Maximum 500 data rows. This tool uses fixed cleanup rules, not AI. Spreadsheet formula prefixes are escaped in the export.
See a workflow your team could use?
Let’s build your version →FROM DEMO TO YOUR BUSINESS
We start with your process and a clear definition of success. A pilot brings the pieces together before a wider rollout.
Scope a pilotChoose the task, inputs and result that matter.
Prepare approved data and agree on system access.
Try realistic cases, including missing information and errors.
Agree on deployment, monitoring, operating costs and maintenance.
CUSTOM DEVELOPMENT · PILOT DISCOVERY
We help teams explore software and workflows that organize suspicious activity, support investigations, reveal operational gaps and make evidence and next steps easier to follow.
These are custom development and pilot-discovery services, not an existing, validated fraud-prevention platform. Teams working on fraud strategy and operations, trust and safety workflows or marketplace integrity can start with one review problem and an agreed way to evaluate it.
Explore tools for organizing refund claims, order information and submitted evidence into a consistent human-review process. Custom review workflows can support refund abuse prevention; their effectiveness must be evaluated.
Scope review queues, case histories, evidence tracking, escalation workflows and documented reviewer decisions. Fraud investigation case management starts with agreed access, responsibilities and evidence review workflows.
Explore ways to surface repeated submissions and related activity across approved data sources. Any matching or risk signals require validation; a flag is not proof of fraud.
Map claim-handling workflows, identify inconsistent review steps and define requirements for stronger operational controls. Policy gap analysis can help teams make review responsibilities and escalation paths explicit.
Develop reporting for claim volumes, review backlogs, escalation rates, recurring patterns and reviewer turnaround time. Measure false positives and appeal outcomes where reliable labeled data exists.
Automate approved administrative steps while keeping consequential claim decisions with authorized reviewers. Claims review automation and human-in-the-loop AI would be scoped and tested around those boundaries.
Our Zendesk pilot and synthetic-data support analytics demo demonstrate adjacent workflow and reporting experience. Neither demonstrates fraud-detection effectiveness. Matching, risk signals and automation would require project-specific validation and a documented human-review process.
Adjacent experience exploring support workflows and human escalation. A support pilot is not evidence of fraud detection or a tested integration with your systems.
A workflow for branded outreach, recipient checks, opt-out handling and send records. It does not establish customer outcomes or capabilities for a new deployment.
ODI TV’s website and inquiry experience show how service information and prepared email requests can fit together.
Explore the ODI TV business websiteA read-only reporting demo using 140 fictional tickets to explore volumes, backlog, response times and escalations. It uses no real customer records or live helpdesk connection and performs no fraud detection or automated claim decisions.
Request a walkthroughODI offers custom AI chatbot and knowledge-assistant development, customer support automation, workflow and API integration, data cleanup, reporting dashboards, sales and booking assistants, and custom business apps with software testing.
Yes. We can scope an assistant around your approved FAQs, documents and product information. The project includes choosing information sources, testing representative questions and defining what happens when an answer is missing.
We assess the available APIs and data formats, then build and test the agreed connections. Examples include sending website inquiries to a CRM, routing support requests and preparing follow-up tasks. Access and integration feasibility are confirmed during scoping.
A focused pilot with a defined task, agreed inputs, a working deliverable and documented test results. Hosting, model costs, production connections and ongoing support are agreed for your project.
No. The conversation is a scripted animation and the workflow tools use fictional records. The CSV cleanup tool works locally in your browser. Live AI models and business integrations are built and tested as part of a separate project.
We can explore integration with your existing support tools. Available APIs, approved access, data formats and system constraints determine feasibility. Connectors, permissions and controls would be agreed, built and tested within the project scope.
Yes. Start with a bounded workflow, approved sample data and agreed evaluation criteria. A pilot can use synthetic or appropriately approved data before any production access is considered. A pilot does not establish production readiness or fraud-detection effectiveness.
Agree on what constitutes a correct review, establish a human-reviewed baseline and use reliable labeled examples where available. Evaluate errors, false positives, missed cases, reviewer agreement and appeal outcomes as appropriate to scope. Without dependable labels, accuracy claims would be premature; results and limitations should be documented before expansion.
No automatic rejection is proposed in these services. Authorized human reviewers retain consequential claim decisions. Administrative automation, reviewer access and escalation controls would be explicitly scoped and tested. A risk flag is a prompt for review, not a finding of fraud.
The requirements depend on the agreed scope and systems. Before access, we would define necessary data, permitted uses, roles, retention, deletion, logging and any third-party processing, then complete your required security and privacy review. Production integrations require approved access and tested controls; no pre-existing compliance certification or enterprise deployment capability is implied.
LET’S DEFINE A USEFUL FIRST STEP
ODI TV · AI & Software Services
Share a high-level challenge and your current tools. Please do not include customer records, passwords or confidential documents.
Illustrative photography: Mohammad Rahmani and Pawel Czerwinski, used under the Unsplash License.