AI systems and automation
AI development and consulting for real business work
AI consulting to choose the right use case, then AI apps, agents, chatbots and integrations your team can supervise. Get a free quote today!
Discuss Your Project
Luminous is an AI development company for businesses that want AI doing real work, not a demo. We start with AI consulting to find the task worth automating, then build the AI applications, agents, chatbots and integrations around the tools your team already uses, with testing and human review built in.
This page covers AI consulting, custom AI development, knowledge tools, document processing and workflow integrations. For the journey from a new inquiry to a booked appointment, see AI revenue systems. For phone calls, see our AI receptionist, and for customer conversations in WhatsApp, see WhatsApp automation.
Our own AI products show the range: LumaTake, an AI-native video content studio for Mac; Text Behind Subject, a browser tool that runs its AI processing on the user's device; and Pindrop, an internal tool that turns natural-language local searches into structured business records. Carlin AI, a dictation and transcription app with AI-assisted editing, is in development; all four are listed on our products page. Our guides to building a knowledge base and AI workflows in n8n show the planning decisions in more detail.
AI consulting: decide what to build first
AI projects that disappoint usually started with a tool rather than a task. Our AI consulting work starts with the work itself: where staff spend time reading, writing, sorting or searching, and which of those tasks has enough real examples to test an AI system against.
- A short list of AI use cases, ranked by time saved, risk and how easy each is to test.
- A build, buy or configure recommendation for each, including when an existing product is the better choice.
- A data and risk review: what the system may read, where it is processed and who checks the output.
- A written plan for the first build, with the test cases that define success.
Consulting can stand on its own. Some teams take the plan to their own developers, and others ask us to build it. If you are weighing a hire against a partner, our guide to hiring an AI engineer or working with an agency sets out the trade-offs.
AI development: what we build
| Build | What it looks like |
|---|---|
| Custom AI applications | Desktop, web and mobile apps with AI at the center, such as LumaTake, our AI-native video studio for Mac. |
| On-device AI | Tools that run models in the browser or on the device, so files never leave it, such as Text Behind Subject. |
| AI agents | Agents that research, prepare records or carry out multi-step tasks within limits you set, such as Pindrop. |
| AI chatbots and assistants | Assistants that answer customers or staff from approved information, on your website, in WhatsApp or on the phone. |
| AI integration services | AI steps added to the software you already run, such as reading incoming documents into your CRM. |
Workflow automation an AI system can take on
| Task | What the system does | Where a person stays involved |
|---|---|---|
| Answering staff questions from policies and manuals | Finds the relevant passages and answers with links to the source. | Reviews questions it could not answer and updates the source documents. |
| Reading forms, contracts or invoices | Extracts agreed fields into your system and flags low-confidence values. | Checks flagged fields before they are used. |
| Drafting replies, summaries or reports | Prepares a draft from the record and your approved wording. | Edits and approves before anything is sent. |
| Moving data between applications | Updates records when an event happens, and retries or alerts when a step fails. | Resolves conflicts the rules cannot decide. |
| Researching and preparing records | Gathers public information into a consistent structure for review. | Decides what to act on. |
Test it before your team relies on it
Before launch, we build a set of test cases from real examples: typical requests, unusual ones and cases the system should refuse or pass to a person. Each version is scored against that set, so a change to a prompt, model or data source is judged on evidence rather than a few good demonstrations.
After launch, the system logs what it did and why. Your team can see which answers were corrected, which sources were missing and where the rules need adjusting.
Choosing the tools
Many integrations fit a workflow platform such as n8n, Make or Zapier. We move to custom code when the logic, volume, data handling or running cost makes a platform awkward. Our comparison of n8n, Make and Zapier sets out those tradeoffs.
We choose the AI model for the task: accuracy on your test cases, cost per run, speed, where the data is processed and the provider's terms. Our guide to evaluating Claude and OpenAI for a business task shows how we compare them.
How an engagement runs
- Pick one task. Agree the work to automate, the people who do it now and what a good result looks like.
- Collect examples. Gather real documents, questions or records for the test set, with sensitive details removed where needed.
- Build and score. Develop the workflow, run it against the test set and review the failures with your team.
- Connect it. Link the approved version to the applications it reads from and writes to, with alerts for failed steps.
- Launch with supervision. Start with a review of every result, then reduce review where the scores and your team agree it is safe.
Access and data handling
The system gets the least access it needs. We agree which data it can read, where that data is processed and stored, how long logs are kept and which provider terms apply. Where information must stay on your own infrastructure or on the device, we design for that from the start.
If the work needs a new internal tool or database rather than an automation, see custom software development. For lead records and follow-up inside a CRM, see CRM setup and automation.
Workflow automation projects come from firms in Philadelphia, San Francisco and Santa Clara County, often starting with one repeated task that eats staff time. Our pages for Denver and Miami show business process automation in two very different settings.
Questions about AI development and consulting
A review of the tasks worth automating, a ranked list of use cases, a build-or-buy recommendation for each and a written plan with test cases for the first build. It can end there or continue into development.
Both. We build complete AI applications, agents and chatbots as well as automations inside existing tools. Our own products, including LumaTake and Text Behind Subject, are examples of the first kind.
Often not. A defined workflow with one or two AI steps is easier to test and cheaper to run. We recommend an agent, which decides its own steps, only when the task varies too much for fixed rules, and we limit what it can change.
The design assumes it sometimes will. Low-confidence results go to a person, important actions need approval, and every run is logged so a mistake can be found and corrected.
Usually not. Most business tasks work with an existing model plus retrieval from your approved documents. That keeps your data out of model training, and updating the system is as simple as editing a document.
The task's complexity, the number of connected systems, the volume of runs and the review needed set the cost. Model and platform usage are separate running costs, which we estimate from the test runs.
Discuss Your Project. Share one repeated task, a few real examples and the applications it touches.