project_cost_estimator.runliveillustrative

1. Project type

Pick the closest match. If you have several ideas, choose the one you would build first.

AI chatbot or assistant on your documentsAnswers questions from your policies, manuals, products or tickets$10k – $50kPredictive model (forecasting, scoring)Demand forecasts, churn or risk scores, lead scoring$10k – $50kAI agent that works in your systemsReads, decides and acts across your tools, with checks in place$10k – $50kComputer vision / object recognitionDetects, counts or inspects items in images and video$10k – $50kGenerative AI content toolDrafts text, images or summaries in your format and tone$10k – $50kRecommendation engineSuggests products, content or next actions for each user$50k – $200kData analytics or BI platformBrings data together for dashboards, reporting and insight$50k – $200kEnterprise AI platformSeveral use cases, large-scale automation, end-to-end deployment$200k+

2. Data readiness

Most of the effort in an AI project sits in getting the data right.

Clean and in one placeOne system or a well-kept database the model can use as it is.Needs cleaning or joiningSpread across systems or spreadsheets, with gaps and duplicates.Needs collecting or labellingNot captured yet, or examples must be tagged by hand first.

3. Integrations

How many systems the AI has to read from or write to.

None, or one modern APIStands alone, or connects to one system with a documented API.2 – 3 systemsFor example a CRM, a helpdesk and a database.Many systems, or legacy without APIsOlder software that needs custom connectors or exports.

4. Hosting and models

Where the model runs and who controls it.

Hosted model APIA commercial model provider, called securely from your application.Private cloud deploymentModels and data kept inside your own cloud account.On-premise or self-hosted modelsOpen models run and tuned on your own servers.

5. Accuracy and oversight

The higher the stakes, the more testing, review and audit trail is needed.

Internal use, standard accuracyStaff use the output and can spot mistakes.Customer-facing, with human reviewCustomers see the output, with a person checking where needed.Regulated or high-stakes decisionsFinance, health, legal or safety, where errors carry real cost.

6. Scale

Who uses it, and how much traffic it has to handle.

One teamA single department or process.Company-wideSeveral teams, roles and permissions.Customer-facing at high volumeThousands of users or requests, with uptime to match.

$ runs in your browsernothing stored or sentassumptions ↓

about this tool

How it works and what it assumes.

The estimator places your project in one of three tiers, using the investment ranges published in our guide, AI/ML Development Services: A Strategic Investment Guide for CEOs & CTOs:

Small AI/ML projects (chatbots, basic predictive models): $10,000 – $50,000. Chatbots, predictive models, AI agents, computer vision and generative AI content tools start here.
Medium-scale projects (data analytics platforms, recommendation engines): $50,000 – $200,000.
Enterprise AI/ML solutions (deep learning, large-scale automation, end-to-end deployment): $200,000+.

Each of the five questions about data, integrations, hosting, oversight and scale scores 0, 1 or 2 points. AI agents and computer vision add one point, because they usually need more testing against real-world cases. The tier range is split into thirds: 0 – 3 points places you in the lower third, 4 – 7 in the middle and 8 or more in the upper third. Enterprise platforms are scoped individually, so they show the starting point only.

Ranges are indicative. They exclude model usage, hosting and taxes, which depend on how much the system is used. A fixed quote follows a scoping call, and we usually suggest a 3 – 6 week proof of concept first so you can test the idea on your own data before committing to a full build. Figures are shown in US dollars; Indian clients are quoted and invoiced in INR. Nothing you enter leaves your browser.

For what sits behind each project type, see our AI chatbot development, machine learning development, AI agent development, object recognition, generative AI, recommendation engine and business intelligence services, or our full range of AI and ML development services.

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