What generative AI development covers
Aranv is a generative AI development company that adds content, image and copilot features to software products and internal tools. We design the feature, choose and test the model, and build the review steps, cost limits and quality checks that keep generated output useful to your users.
Generative AI creates new text, images or code from instructions. This page covers features built into a product or tool your users already open. Complete AI-based applications and internal tools sit under AI software development, and software that plans and carries out tasks by itself under agentic AI and multi-agent systems.
A generative feature involves more than a model call. We design prompts and templates, supply the facts the model needs, often through retrieval from your own content, and check outputs before anything reaches a customer.
What sits behind a generative feature
A prompt is only the start
- 01 Define the output Agree what a good result looks like, using real examples from your business.
- 02 Ground it in your data Product details, brand rules and templates go to the model with each request.
- 03 Check before it ships Automated tests and human review catch wrong facts, off-brand tone and broken formatting.
- 04 Measure and tune Track acceptance, edits and cost per request, then adjust prompts or models.
Model choice follows the task, budget and data rules, and can change later without a rebuild.
Gen AI app development inside your products
Generative features work best inside a workflow people already follow, not in a separate chat window.
AI content generation
Product descriptions, listings, emails and reports drafted from your data and brand rules, then approved by staff.
Image generation and editing
Product shots on new backgrounds, banner variants or mockups produced from approved assets and templates.
In-app copilots
An assistant inside your software that drafts replies, fills forms or explains a record to the user looking at it.
Summaries and rewriting
Long notes, call transcripts or documents condensed into the format each team needs, such as a handover note.
Output checks and review queues
Each draft is tested before a person sees it: required facts present, banned claims absent, length and format right. Drafts that fail go back to the model or into a review queue, and every approval or edit is logged, which shows which prompts need work.
What we do
- Rule checks on every output
- Review queue with approve and edit
- Source data shown beside each draft
- Logged edits and rejections
- Test set built from real examples
Image tools for catalogs and campaigns
Image features start from photos and templates you already own. We build tools that place a product on a new background, resize one banner for every channel or produce mockup variants, with brand rules for colors and logos and a staff member choosing what goes live.
What we do
- Background swaps for product photos
- Banner sizes for each channel
- Mockups from approved templates
- Brand color and logo rules
- Approval step before publishing
How we approach a project
We work with models from OpenAI, Anthropic and Google as well as open-weight models, chosen per task on quality, speed, cost and where your data may be processed. The LLM integration layer lets one model replace another without rewriting the feature, since providers retire model versions regularly.
Risk management is part of the design. The NIST Generative AI Profile is a practical reference for invented facts, privacy and intellectual property. If you are still deciding where the technology fits, generative AI consulting can come first.
Bring one content or copilot idea to a 30-minute session with our AI team, which you can book on our contact page.
How we work
- A person approves what matters
- Tested against real examples
- Cost visible per request
Our Process
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Use case and examples
Pick one task, collect good and bad examples, and set the bar a draft must clear.
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Prototype with real data
Test prompts and models on your examples, comparing quality, speed and cost.
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Guardrails and review
Add grounding, output checks, approval steps and logging inside the product.
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Release and refine
Launch to a pilot group, track edits and rejections, and tune prompts or models from what you learn.
Why Aranv for generative AI
Models compared on your examples
We run two or three candidate models against your own sample tasks before building, then choose on quality, speed, cost and where the data is processed.
Spend visible from day one
Tokens and cost are logged per request from the prototype onward, so you see the running cost before launch and can cap usage per user or per day.
Built inside your codebase
We work in your repository and release process, so generated features ship with the same code review, tests and deployment steps as the rest of your product.
Review ownership agreed upfront
Before launch we settle which outputs a person must approve, who that person is and what happens to a rejected draft, and the product enforces those rules.
Planning Generative AI Development?
Book a 30-minute conversation with the team that would build it.
Generative AI FAQs
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What can generative AI add to our product?
Common features include drafting listings, emails or reports from your data; creating or editing images from approved assets; summarizing long records; and in-app copilots that help users fill forms or find answers. A good first feature is a frequent task where a draft saves time and a person can check it quickly.
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How do you stop generative AI from making things up?
No method removes the risk completely, so we combine several. The model receives the facts it needs with each request, outputs are checked against rules and source data, and anything customer-facing or high-stakes goes through human approval before it is used.
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Will our data be used to train the AI model?
Business API terms from the major providers generally say customer inputs are not used for training by default, but terms differ and change, so we confirm them for your chosen provider and region. Where data must stay in-house, an open-weight model can run on infrastructure you control.
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What does a generative AI feature cost to run?
Running cost depends on how often the feature is used, how much text or how many images each request involves and which model handles it. We estimate cost per request during the prototype, then use caching, smaller models for simple steps and usage caps to contain it.
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Do we need generative AI consulting before development?
Only if you are unsure where it fits. A short consulting phase reviews your workflows and data, ranks candidate use cases by value and risk, and picks one to prototype. If you already know the feature you want, we can start the prototype straight away.
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Can we copyright content the AI generates?
Not automatically. The US Copyright Office’s view is that AI output is protected only where a person has decided enough of its expressive content, and prompts alone do not count. Staff editing and arranging drafts helps; for anything important, ask your own lawyer.
Let’s talk about your next step
Contact Aranv to discuss your workflow, current tools, and goals. Meet with our team or send an inquiry.
Book a meeting with our team
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