For startups
AI Solutions
An AI feature is useful when it takes a real task off someone's plate and fails in a way a person can see. Vortex Digital helps startups add assistants, retrieval, and workflow automation inside a product, with limits, review, and an honest account of cost.
- LLM integration
- Retrieval
- Human review
- Usage cost
What we build
These are kinds of features a product can include. They are design options, not measured results, and not a claim of an autonomous system.
Assistants inside the product
A chat or command surface that answers from approved sources and hands off when it should not decide.
Retrieval over your content
Search that pulls from documents or records you control, then asks a model to answer from that material.
Workflow assistance
Sorting, drafting, or extracting fields where a person still confirms the result before it changes the system of record.
Document handling
Reading uploads to suggest structure or summaries. The original file remains available, and a wrong extraction can be corrected.
Challenges we address
Model demos hide the product problems. The work is those problems.
01
A demo with no source of truth
A chat window that invents answers is not a feature. Retrieval, citations, and a refusal path come before the interface polish.
02
Nobody can tell a bad answer from a good one
Evaluation needs sample tasks and a person who knows the domain. We do not publish accuracy percentages the project has not measured.
03
Customer data leaves the product by accident
Prompts, logs, and vendor retention have to be chosen on purpose. Privacy is a design constraint, not a footer.
04
The bill arrives after the demo
Tokens, vector storage, and retries cost money. A proposal should say what drives usage, not pretend the feature is free to run.
Our technical approach
Start with the task and the source material. Add a model only where it changes that task, and keep a person in the loop where a mistake is expensive.
01
Task definition
Name the job, the input, the allowed sources, and what the system must refuse.
02
Architecture
Decide what is retrieved, what is sent to a model, what is stored, and which vendor or hosting option fits the privacy constraint.
03
Integration
Put the feature in the existing application, with the same accounts and audit trail the rest of the product uses.
04
Evaluation
Review a set of real examples with someone who knows the right answer. Track failures. Do not treat a single demo as proof.
05
Operations
Log usage, cap runaway calls, and leave a path to disable the feature without taking down the product.
Service capabilities
LLM integration
A model API behind an application boundary, with timeouts, retries, and a non-AI path when the provider fails.
Retrieval-augmented generation
Chunking, indexing, and answers grounded in content you select. The index is not a dump of every file the company owns unless that is an explicit choice.
Intelligent search
Query the product's own records with a mix of keywords and vectors when that improves finding, and show the source.
Human oversight
Confirm, edit, or reject model output before it becomes a permanent record. High-impact actions are not left to an unattended loop.
Privacy and data handling
What is sent, what is logged, and how long a vendor may retain it. The legal review of that choice still belongs to the company and its counsel.
Cost visibility
A rough model of what drives spend, and controls such as limits per user or per day. Not a guarantee of a future invoice.
Typical deliverables
These are examples of what a proposal can include. Scope decides which ones you receive. None of them is an automatic part of every inquiry.
- A written task for the AI feature, including what it must not do
- The integration agreed in the proposal
- A retrieval or workflow design when that is the approach
- A small evaluation set and a way to review failures
- Notes on data sent to vendors and what drives usage cost
Technologies and tools
Names below come from the working catalog in the site configuration. A project may use some of them, or a different set, once the product is known. Listing a tool is not a certification, a vendor partnership, or a promise that every engagement includes it.
AI
- LLM APIs
- RAG
- AI agents
- Vector search
Backend
- Node.js
- Python
- Go
Data
- PostgreSQL
- MySQL
- Redis
Questions founders ask
Will the system be correct on its own?
No. Models make mistakes. We design for review, refusal, and a record of failures. We do not promise an error-free or fully autonomous system, and we do not invent accuracy figures.
Can you add AI to software we already have?
Often, if there is a clear task and source material. We look at the product and the data first. A chatbot pasted on the side of an app is not the default recommendation.
How should we think about privacy?
Decide which data may leave your systems, what the model vendor stores, and who can see prompts and outputs. We can implement that decision. Counsel should review it.
What does an AI feature cost to run?
It depends on volume, model choice, and how much text you retrieve. We can estimate the drivers during planning. The website cannot quote a bill.
Do you train a private model?
Most startup products need integration and retrieval, not a model trained from scratch. Training is a different project and is not assumed here.
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