# Custom AI software development company: AI software you own, built around how you work

URL: https://vrisic.com/services/custom-ai-software-development/
Last updated: 2026-10-06

> As an AI software development company, we design, build and run custom AI software for companies in the US, UK and UAE: operations platforms, client portals, internal tools and AI native products, built around the way you work. A one time, fixed price investment, the source code lives in your repository and the system runs in your own cloud.

## What is custom AI software development?

Custom AI software development is the design and engineering of a complete application, such as a SaaS product, an internal operations platform, a customer portal or a copilot inside an existing product, where large language models are part of the core design rather than a bolt on. The software has its own interface, users, permissions, data model and business logic, and the AI reads, reasons, drafts and acts inside it. Vrisic is an AI software development company that builds these products end to end, and you own the code.

| What we build | AI native web apps, SaaS products, internal platforms, customer portals and in product copilots |
| --- | --- |
| First release | First version (MVP) in 8 to 12 weeks |
| Typical budget | $25,000 to $150,000; enterprise platforms from $150,000 |
| Models | GPT 5 family, Claude, Gemini, Llama and Mistral, chosen per task |
| Hosting | Your own AWS, Azure or Google Cloud account |
| Ownership | You own the code, prompts, evaluation sets and data |

On this page

1. Overview
2. What we build
3. Architecture
4. Build vs buy
5. Industries
6. Process
7. Pricing
8. Cost drivers
9. Tech stack
10. Security
11. US, UK, UAE
12. Choosing a partner
13. Ownership

## Types of custom AI software our AI software development company builds

Most custom AI solutions fall into six shapes. Knowing which one you need sets the architecture, the budget and the first release.

1. 01 AI native SaaS products A product you sell, where the AI is the reason customers pay. We build the whole thing: sign up, organisations and roles, Stripe billing, usage metering, an admin console and the AI features at the centre. Multi tenancy is designed in from the first sprint so one customer’s data never reaches another customer’s prompts. Usage based pricing is modelled against real model costs before launch, so your margins hold as you grow. **Best for:** Founders and software companies launching a new AI product or product line Tenant isolation in the database and the retrieval index Per customer usage and cost tracking Stripe subscriptions or usage billing Public API and webhooks for your customers
2. 02 Internal operations platforms One place where your team works cases, claims, orders, applications or projects, with AI doing the reading, sorting and first drafts. These replace a patchwork of spreadsheets, inboxes and three different tools. The AI classifies incoming work, extracts fields from documents, suggests the next action and drafts replies, while people approve anything that affects money, customers or compliance. **Best for:** Operations, finance, claims and service teams handling high volumes of similar work Queues, assignments and service level timers Document extraction with confidence scores Approval steps and a full audit trail Reports your managers actually read
3. 03 Customer and partner portals Secure portals where clients upload documents, track progress, ask questions and get answers without waiting for your team. The AI checks uploads for completeness, answers questions from the client’s own file and your published policies, and escalates to a named person when it is unsure. Your staff see the same case from an internal view with everything the portal collected. **Best for:** Professional services, lenders, insurers, property managers and healthcare providers Single sign on or magic link login Answers scoped to the logged in client only Upload checks before a human ever looks Clear handoff to your team
4. 04 Copilots inside an existing product An assistant that lives inside your software and understands your product data. Users ask questions in plain language, generate reports, configure settings or take actions without leaving the app. We build it as a module in your codebase, respecting the permissions your product already has. If you only need AI added to a system you did not build, our [AI integration services](https://vrisic.com/services/ai-integration/) are the better fit. **Best for:** Software companies adding AI features to a product customers already use Uses your existing roles and permissions Actions go through your own API, never around it Streaming answers with sources Feature flags for a gradual rollout
5. 05 Document intelligence applications Software that reads contracts, invoices, medical forms, bank statements or technical manuals and turns them into structured data, summaries and checks. We combine OCR, layout aware parsing and language models with validation rules, so a total that does not add up is flagged rather than passed through. Reviewers work from a side by side view of the source page and the extracted fields. **Best for:** Legal, finance, insurance, logistics and healthcare teams drowning in paperwork Field level confidence and source highlighting Rules that catch arithmetic and date errors Batch processing with retries Export to your system of record
6. 06 Agentic back office systems Software where [AI agents](https://vrisic.com/services/ai-agent-development/) carry out multi step work across your tools: gathering information, updating records, sending messages and scheduling follow ups, with a person approving the risky steps. We give the agents a small set of well defined tools, strict limits on what each can change, and a replayable log of every decision so you can see why something happened. **Best for:** Teams with repeatable processes that span three or more systems Tool permissions scoped per agent Human in the loop approvals Replayable traces for every run Budgets on steps, time and spend

## How a production AI application is put together

A demo is a prompt and a text box. A production AI application has eight layers, and most failures we are asked to fix come from skipping three of them.

When we scope a custom AI software development project, we draw the same eight layers every time. The model is only one of them, and it is usually the easiest to change later.

- **Frontend.** A React or Next.js interface that streams answers, shows sources, handles long running jobs and makes it obvious when the AI is unsure. Good AI interfaces show their working and make correction easy.
- **Backend and business logic.** A Python (FastAPI) or Node.js service that owns authentication, roles, workflows and every write to your data. The model never talks to your database directly.
- **Data layer.** PostgreSQL for records, object storage for files, and clean pipelines that bring data in from your CRM, ERP or document stores on a schedule or by event.
- **Model layer.** A gateway that routes each task to the right model, applies timeouts, retries and fallbacks, redacts personal data where needed and records tokens and cost per request.
- **Retrieval.** Hybrid keyword and vector search (for example pgvector or Azure AI Search) with reranking and permission filters, so answers come only from documents the user is allowed to see. Our [enterprise RAG and AI search](https://vrisic.com/services/enterprise-rag-ai-search/) work goes deeper here.
- **Agents and tools.** Where the software needs to act, an orchestration layer such as LangGraph or the OpenAI Agents SDK, with tools exposed through typed functions or Model Context Protocol servers.
- **Evaluation.** A test set of real inputs with expected outputs, scored automatically on every change, plus human review of samples each week.
- **Observability.** Traces of every prompt, retrieval and tool call in Langfuse or LangSmith, linked to OpenTelemetry metrics, with alerts on quality, latency and spend.

The three layers teams most often skip are evaluation, observability and the model gateway. Without them you cannot tell whether a prompt change made things better or worse, you cannot explain a bad answer to a customer, and one runaway loop can cost more in a night than the feature earns in a month. Our [AI architecture and engineering](https://vrisic.com/services/ai-architecture-engineering/) practice exists largely to put those three in place.

Rule of thumb

Spend roughly a fifth of the build on evaluation and observability. It feels slow in week three and pays for itself the first time a model provider changes behaviour under you.

## Custom AI software, off the shelf AI or a no code builder?

Build vs buy is not a matter of taste. Off the shelf wins on speed and upfront cost; custom wins on fit, ownership and control of unit costs. Here is an honest side by side.

|  | Off the shelf AI SaaS | No code or low code builder | Custom AI software by Vrisic |
| --- | --- | --- | --- |
| Time to a first working version | Hours to days | Days to a few weeks | 8 to 12 weeks for an MVP |
| Upfront cost | Low, usually per seat | Low to moderate | From $25,000 |
| Fits your exact workflow and data model | Partly | Within platform limits | Yes |
| You own the code and roadmap | No | No | Yes |
| Deep integration with your own systems | Prebuilt connectors | Connectors and webhooks | Yes |
| Control over model choice and cost per request | No | Some | Yes |
| Evaluation and audit trail you control | No | Basic run logs | Yes |
| Can become a product you sell | No | Rarely | Yes |
| Ongoing maintenance on your side | Vendor handles it | Low | Ours on a support plan, or your team |
| Best when | The task is common and done the standard way | Internal prototypes and simple flows | The software is part of how you compete |

If a mature tool already covers 80% of the job, buy it and spend your budget elsewhere. Our [build vs buy AI guide](https://vrisic.com/blog/build-vs-buy-ai-software/) works through the three year costs side by side, and we compare the options in more depth in AI agent builders vs custom development and automation tools vs custom builds.

## Custom AI solutions by industry

The architecture stays similar across sectors. What changes is the data, the regulation and the one metric the business watches.

- [Healthcare and dental](https://vrisic.com/industries/healthcare/): Patient intake portals, referral triage, clinical document summaries and scheduling platforms built to HIPAA requirements, with protected health information kept out of logs.
- [Law firms](https://vrisic.com/industries/law-firms/): Matter intake platforms, contract review workbenches and knowledge tools that search precedents and firm templates with citations back to the source clause.
- [Real estate](https://vrisic.com/industries/real-estate/): Lead qualification platforms, listing content generators and tenant portals that answer lease questions and route maintenance requests.
- [Ecommerce and retail](https://vrisic.com/industries/ecommerce/): Catalogue enrichment pipelines, shopping assistants grounded in live inventory, and returns platforms that read photos and order history.
- [Home services](https://vrisic.com/industries/home-services/): Dispatch platforms that turn calls and texts into booked jobs, quote builders and technician copilots that read equipment manuals on site.
### Financial services and insurance

Claims workbenches, underwriting document review and KYC file assembly with explainable decisions and full audit trails.

Shorter cycle time per case

### Logistics and supply chain

Exception management platforms that read carrier emails, bills of lading and customs documents and update shipment records.

Fewer manual touches per shipment

### B2B software companies

In product copilots, AI reporting and natural language configuration shipped as features of an existing SaaS product.

Retention and expansion revenue

## How we deliver custom AI development services

Six steps, with working software every two weeks and a fixed price agreed before the build starts.

1. 01 Scoping call and written proposal 1 week A free call to understand the problem, the users and the systems involved. We come back with a written proposal: scope for the first release, architecture outline, risks, a fixed price and a timeline. If AI is not the right answer, we say so here. **You get:** Scope document, Fixed price, Risk list
2. 02 Discovery and design 2 to 3 weeks Workshops with the people who will use the software. We map user journeys, design the data model, produce clickable designs and collect 50 to 200 real examples that become the first evaluation set. Data access is sorted now, not in week eight. **You get:** User journeys, Clickable prototype, Evaluation set v1, Architecture decision record
3. 03 Technical spike on the hardest AI task 1 to 2 weeks Before building screens, we prove the riskiest AI capability against your real data, compare two or three models on the evaluation set and report accuracy, latency and cost per request. You decide whether to proceed with numbers in front of you. **You get:** Model comparison report, Cost per request estimate
4. 04 Build in two week sprints 4 to 12 weeks We build the product in short cycles and demo working software at the end of each one. Authentication, roles, integrations and AI features grow together, and the evaluation set runs automatically on every pull request. **You get:** Working releases every two weeks, Automated tests, Sprint notes
5. 05 Hardening and launch 1 to 2 weeks Security review, prompt injection testing against the OWASP Top 10 for LLM Applications, load testing, cost limits, alerting and a staged rollout to a first group of users. We write the runbook your team will use when something looks wrong. **You get:** Security review notes, Runbook, Monitoring dashboards
6. 06 Run, measure and improve Ongoing After launch we watch quality, latency and spend, review a sample of real conversations each week and ship improvements. You can keep us on a support plan, move to a retainer, or take the product fully in house with a handover. **You get:** Monthly quality report, Release notes

## Custom AI software development cost and pricing

Typical ranges for the three kinds of engagement we run. You get one fixed price in writing after a free scoping call, and it does not move unless the scope does.

### First version (MVP)

Proving a product idea or one high value workflow with real users

$25,000 to $60,000 £20,000 to £48,000 AED 92,000 to AED 220,000

8 to 12 weeks

- Discovery, design and a clickable prototype
- One core AI capability with an evaluation set
- Web app with login and basic roles
- One or two integrations
- Cloud deployment in your account
- Handover documentation

### Production platform

A product or internal platform used every day by real customers or staff

$60,000 to $150,000 £48,000 to £120,000 AED 220,000 to AED 550,000

12 to 20 weeks

- Several AI features, agents or retrieval
- Roles, single sign on and audit logs
- Three to six integrations
- Evaluation in CI and live monitoring
- Security review and load testing
- Admin console and usage reporting

### Enterprise platform

Regulated or multi department platforms with strict security and scale needs

From $150,000 From £120,000 From AED 550,000

5 to 9 months

- Multi tenant or multi region architecture
- Data residency and private networking
- Compliance documentation support
- Formal red teaming and evaluation
- Phased rollout across teams
- Dedicated engineering lead

- [Not ready to commit to the full build? Start with a 10 day AI Pilot on your own data for $1,500 (£1,200, AED 5,500). The full fee is credited if you continue.See the pilot](https://vrisic.com/ai-pilot/)
Typical ranges; you get one fixed price in writing after a free scoping call. Model and hosting usage is billed at cost, and managed support plans start at $1,500 per month. For context, Clutch reports an average AI development project of about $120,600 based on verified client reviews ([Clutch AI pricing guide, updated September 2026](https://clutch.co/developers/artificial-intelligence/pricing) ). Our full breakdown is in [how much AI development costs](https://vrisic.com/blog/ai-development-cost/) .

## What drives the cost of AI software development?

Two projects with the same one line description can differ in price by three times. These are the reasons, roughly in order of impact.

| Cost driver | Effect on budget | Why it matters |
| --- | --- | --- |
| Number of integrations | High | Every system the software reads from or writes to needs authentication, mapping, error handling and tests. Old systems without clean APIs cost the most. |
| Accuracy target and consequence of errors | High | A drafting tool a person always edits needs less testing than software that sends invoices or medical advice. Higher stakes mean larger evaluation sets and approval steps. |
| Compliance scope | High | HIPAA, UK GDPR or UAE data residency add design work, documentation, private networking and review time on top of the core build. |
| User roles and screens | Medium | Each distinct role (customer, agent, manager, admin) adds views, permissions and test cases. A single role internal tool is much simpler than a multi tenant SaaS. |
| Data quality | Medium | Clean, well labelled data shortens the build. Scanned PDFs, duplicate records and missing fields mean pipeline and clean up work before AI can use them. |
| Agent autonomy | Medium | Software that only suggests is cheaper to make safe than software that acts. Each action an agent can take needs limits, logging and a rollback path. |
| Hosting model | Medium | Managed APIs are fastest. Self hosted open weight models on your own GPUs or in a private cloud add infrastructure work and ongoing cost. |
| Design polish | Low | An internal tool can use a clean component library. A consumer facing product needs custom design, onboarding and more usability testing. |

## Have a product idea or a process that no tool fits?

Tell us what you want to build in a 30 minute scoping call. You leave with an honest view on feasibility, a rough architecture and a price range, whether or not you work with us.

- [Book a free scoping call](https://vrisic.com/contact/)
## The technology stack behind our AI software development

Proven, widely supported tools that your own engineers or another partner can maintain. We avoid anything that would tie you to us.

### Frontend and product

React, Next.js, TypeScript, Tailwind CSS, React Native

Streaming interfaces, accessible components and mobile apps when the product needs them.

### Backend and data

Python, FastAPI, Node.js, PostgreSQL, Redis, Temporal

Durable workflows for long running AI jobs, with retries that survive restarts.

### Models

GPT 5 family (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta), Mistral

Chosen per task on your evaluation set, not by habit.

### Retrieval and search

pgvector, Pinecone, Weaviate, Qdrant, Azure AI Search

Hybrid search with reranking and permission filters.

### Agents and orchestration

LangGraph, LlamaIndex, OpenAI Agents SDK, Model Context Protocol

Explicit state machines for agents, so behaviour is testable.

### Cloud and observability

AWS Bedrock, Azure OpenAI, Google Vertex AI, Langfuse, LangSmith, OpenTelemetry

Deployed with infrastructure as code in your own account.

## Security and compliance built into the product

AI adds new ways for software to leak data or do the wrong thing. We design for them from the first sprint.

### Your cloud, your keys

We deploy into your AWS, Azure or Google Cloud account with encryption at rest and in transit. Secrets live in your key vault, and our access is removed at handover or whenever you ask.

### Model provider data terms

We use business API tiers from OpenAI, Anthropic, Google, AWS Bedrock or Azure OpenAI that do not train on your API data under their standard terms, and we document retention settings for each provider.

### PII redaction and minimisation

Personal data is masked before it reaches a model where the task allows, and prompts and traces are stored with the same retention rules as the rest of your records.

### Permissions the AI cannot bypass

Retrieval and tool calls run with the logged in user’s permissions. Agents get only the tools they need, and every write goes through your backend rules.

### Audit logs and prompt injection defence

Every prompt, retrieved document and action is logged. We test against the [OWASP Top 10 for LLM Applications](https://genai.owasp.org/llm-top-10/) before launch.

### Working to your frameworks

We can build to HIPAA requirements and sign a BAA where our vendors support it, and design for GDPR, UK GDPR, CCPA/CPRA, UAE PDPL and the [EU AI Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) . We do not claim certifications we do not hold.

## AI software development company for the US, UK and UAE

Same engineering standards everywhere. The differences are law, language, data residency and the hours we overlap with your team.

US

### United States

US clients mostly care about HIPAA for health data, state privacy laws led by California’s CCPA/CPRA, and SOC 2 expectations from their own customers. California’s privacy regulator finalised rules on automated decisionmaking technology in 2025 ([CPPA, September 2025](https://cppa.ca.gov/announcements/2025/20250923.html) ), so we design explainability and opt out paths where AI drives significant decisions.

- US cloud regions by default
- BAA route for healthcare builds
- Pricing and invoices in USD
- Working hours overlap with Eastern and Pacific time

UK

### United Kingdom

UK projects run under UK GDPR and the Data Protection Act 2018, as amended by the Data (Use and Access) Act 2025. We complete data protection impact assessments with you, keep data in UK or EU regions, and use British English in the product when your users expect it. Our [guide to choosing an AI development company in the UK](https://vrisic.com/blog/ai-development-company-uk/) covers local buying questions.

- AWS London or Azure UK South hosting
- DPIA support and records of processing
- Pricing in GBP
- Full overlap with UK working hours

UAE

### United Arab Emirates

UAE builds often need Arabic and English interfaces with right to left layouts, Arabic capable models and hosting inside the country. We work to the UAE PDPL (Federal Decree Law No. 45 of 2021), the DIFC Data Protection Law No. 5 of 2020 or ADGM rules depending on where you are licensed. More in our [Dubai and UAE AI development guide](https://vrisic.com/blog/ai-development-company-dubai-uae/) .

- Azure UAE North or AWS Middle East (UAE) region hosting
- Arabic and English, right to left interfaces
- Pricing in AED
- Good overlap with Gulf Standard Time

## How to choose an AI software development company (and what the best ones do)

Eight questions that separate teams who ship reliable AI software from teams who ship demos. Ask them of every vendor, including us.

1. 1 Will you show us how you evaluate AI quality? Ask to see an evaluation set and a report from a past build. A vendor who measures quality only by trying a few prompts will not notice when a model update breaks your product.
2. 2 Who owns the code, prompts and evaluation data? The answer should be you, in writing, with the repository in your organisation from day one. Watch for platform fees or proprietary frameworks that make leaving expensive.
3. 3 Where will the software run, and who holds the keys? Your cloud account and your secrets vault is the safe default. Vendor hosted setups are fine for pilots but should come with a clear migration path.
4. 4 How do you control model costs per user? Good teams can tell you the expected cost per request before launch and show you dashboards after it. Rate limits, caching and model routing should be standard.
5. 5 What happens when the AI is wrong? You want a clear answer about confidence thresholds, human review, fallbacks and how a user reports a bad answer. Silence here is the biggest red flag.
6. 6 Can we talk to the engineers who will build it? Many firms sell with senior people and deliver with junior ones. Meet the actual team and ask them a technical question about your use case.
7. 7 Is the price fixed, and what changes it? A fixed price for a defined scope protects you. Ask how change requests are priced and whether discovery is a separate paid phase.
8. 8 How will our team take over? Look for documentation, runbooks, recorded walkthroughs and a handover period. You can compare vendors further in our list of the best AI software development companies.

## Code ownership, IP and support after launch

You should be able to fire any vendor, including us, the day after launch and keep running. Here is how we make that true.

All intellectual property in the work we deliver is assigned to you on payment. That includes the application code, infrastructure as code templates, prompts, system instructions, evaluation sets, synthetic test data and design files. Nothing is licensed back to you. The only third party components are open source libraries under permissive licences and the model APIs you pay for directly.

Your data stays yours as well. We do not reuse it to build products for other clients, and we do not send it to any model provider that trains on business API traffic. If you want to fine tune a model later, the training data and resulting weights belong to you, and we explain the tradeoffs in RAG vs fine tuning before anyone spends money on it.

After launch you choose how much of us you want:

- **Managed support** from $1,500 per month: monitoring, incident response, model and library updates, monthly evaluation reruns and small improvements.
- **Engineering retainer** from $4,000 per month for a steady roadmap of new features.
- **Full handover** to your team with documentation, runbooks and recorded walkthroughs, plus a few weeks of paired support.

Whichever you pick, plan for models to change. Providers retire versions, and a newer model can behave differently on your prompts. Because every AI feature has an evaluation set, upgrading is a measured decision rather than a gamble. You can see how we run engagements day to day on our [how we work](https://vrisic.com/how-we-work/) page.

## Related services and guides

Custom AI software usually combines several of the capabilities below.

- [AI Agent Development](https://vrisic.com/services/ai-agent-development/): Agents that complete multi step work inside your platform.
- [Generative AI Applications](https://vrisic.com/services/generative-ai-development/): Focused copilots for drafting, summarising and analysis.
- [Enterprise RAG & AI Search](https://vrisic.com/services/enterprise-rag-ai-search/): Permission aware answers from your company knowledge.
- [AI Architecture & Engineering](https://vrisic.com/services/ai-architecture-engineering/): Evaluation, hardening and architecture reviews.
- [AI development cost guide](https://vrisic.com/blog/ai-development-cost/): Detailed pricing by project type and region.
## Questions we hear every week

Still unsure about something? Ask us on a call. We will give you a straight answer, even if it means we are not the right partner.

- [Ask a question](https://vrisic.com/contact/)
### How much does it cost to develop AI software?

Custom AI software usually costs $25,000 to $60,000 for a first version (MVP), $60,000 to $150,000 for a production platform, and from $150,000 for an enterprise platform. The spread comes from the number of user roles, integrations, compliance needs and accuracy targets. Running costs after launch typically add 15% to 30% of the build cost per year. Vrisic gives you one fixed price in writing after a free scoping call.

### Can I build my own AI software?

Yes, if you have engineers who can build a normal web application and are willing to learn evaluation, retrieval and model operations. Prototypes are easy with the OpenAI, Anthropic or Google APIs. The hard part is production: permissions, test sets, cost control, monitoring and security. Many teams build the product themselves and bring in a partner for architecture, evaluation and the first hardened release.

### Is there an AI that can develop software on its own?

Coding assistants such as GitHub Copilot, Cursor and Claude Code write a large share of routine code, and we use them daily. They do not decide what to build, design a data model around your business, judge security trade offs or take responsibility for a release. Experienced engineers still own architecture, review and testing, and that is where most of the value in a custom build sits.

### How long does custom AI software development take?

A first version (MVP) takes 8 to 12 weeks, a production platform 12 to 20 weeks, and an enterprise platform 5 to 9 months. The first two weeks are discovery and design, and you see working software every two weeks after that. Timelines stretch mainly when data access, security reviews or stakeholder sign off take longer than planned, so we surface those dependencies in week one.

### Who owns the code and IP when Vrisic builds our AI software?

You do. Our contract assigns all intellectual property in the delivered work to you on payment, including source code, prompts, evaluation sets, infrastructure templates and documentation. The code lives in your GitHub or GitLab organisation and runs in your cloud account from the first day, so there is nothing to hand back and no licence fee to keep using what you paid for.

### What does an AI software development company actually do?

An AI software development company turns a business process or product idea into working software where AI does part of the job. That covers discovery, interface design, backend and data engineering, model selection, retrieval over your data, agent logic, evaluation, security, cloud deployment and support. A good one also tells you where AI is the wrong tool and a simple rule or form will do.

### Do you work with small businesses and startups, or only enterprises?

Both. Founders and small businesses usually start with a first version (MVP) in the $25,000 to $60,000 range to prove demand or a workflow before spending more. Larger organisations tend to start at the production platform tier because they need single sign on, audit logs and integrations from day one. The process is the same; the scope is what changes.

### Which AI models do you use, and can we switch later?

We choose per task from the GPT 5 family by OpenAI, Claude by Anthropic, Gemini by Google, and open weight models such as Llama and Mistral when data must stay in your network. Every model call goes through a thin gateway layer in your code, so switching providers means changing configuration and rerunning the evaluation set, not rewriting the product.

### How do you stop the AI from making things up?

We ground answers in your approved data through retrieval, require citations where facts matter, and constrain outputs with schemas so the model returns structured fields rather than free text. Every feature has an evaluation set of real examples scored before launch and after each change. For high stakes steps, a person approves the output before anything leaves the system.

### Can you take over an AI prototype someone else built?

Yes. We start with a two to four week review of the code, prompts, data flows and hosting, then give you a written list of what to keep, fix or rebuild. Many prototypes have a sound idea but no evaluation, no permissions and no cost limits. Hardening them is often cheaper than starting again, and we will tell you plainly if it is not.

### What does it cost to run custom AI software after launch?

Expect model and hosting usage, billed at cost, plus maintenance. Across published sources the annual running cost is typically 15% to 30% of the build cost. Managed support plans from Vrisic start at $1,500 per month and cover monitoring, model updates, evaluation reruns and small improvements. We add per user and per request cost dashboards so there are no surprise bills.

### Should we build custom AI software or buy an off the shelf AI tool?

Buy when a mature product already does the job the standard way and your data fits its model. Build when the workflow is part of how you compete, when you need AI across several of your own systems, or when you want to sell the result as a product. A common middle path is buying for commodity tasks and building the one platform that differentiates you.

## Find the process where AI will pay off first

Message us on WhatsApp for the fastest reply, or book a free thirty minute call. You will leave with the processes most worth automating, whether to build new or upgrade what you have, a realistic timeline and a clear idea of cost, whether or not you work with us.

- [Chat on WhatsApp](https://wa.me/916377767206?text=Hi%20Vrisic%2C%20I%20found%20you%20on%20your%20website%20%28your%20services%29.%20I%20would%20like%20to%20talk%20about%20an%20AI%20project.)
- [Book a free strategy call](https://vrisic.com/contact/)
No obligation. NDA available on request.

WhatsApp replies are usually fast; email within one business day.

- [Or start with a 10 day AI Pilot for $1,500, credited in full](https://vrisic.com/ai-pilot/)

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