Mira AIby Miracuves
AI services and ready-made AI platforms Build AI that thinks, speaks, sees, predicts and acts and own every line of it.
Custom AI, LLM and RAG, agents, voice, vision and machine learning, built by Miracuves engineers. Or launch a ready-made AI platform under your brand in 6 working days. Every build ships with 100% of the source code.
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First response under 2 hours, Mon-Sat 10:00-19:00 IST
- 9,000+projects delivered since 2010
- 6,000+clients served
- 3,900+apps published
- 20+countries
- 24AI and data servicesFrom LLM apps to MLOps
- 8ready-made AI platformsUnder your brand
- 6working daysTo launch a ready-made platform
- 100%source code ownershipOn every build
One team, every layer
An AI product is four layers. Mira AI builds all of them.
- 01Your data
Documents, databases, CRM records, calls and images, cleaned and connected with access rules intact.
- 02Models
GPT, Claude, Gemini, Llama or a model trained on your own history, picked per task on quality and running cost.
LLM DevelopmentMachine LearningComputer VisionNLP DevelopmentMLOps
- 03Agents and logic
Retrieval, tools, workflows and checks that turn a model into something that finishes real work.
- 04Your product
Chat, voice, apps and dashboards your users touch, under your brand, with the full source code.
Chatbot DevelopmentAI Voice AgentsChatGPT CloneAI Development
Route finder
What do you want AI to do?
Pick a goal. Mira maps the route, the timeline and where to start.
Mira suggests
Start from a ready-made AI platform that already works: an AI assistant, an avatar video studio or a prompt-to-app builder. We put your brand on it and take it live, and the source code is yours.
Start here
Mira suggests
Add search, summaries, drafting or an assistant to your app, CRM, ERP or helpdesk without a rebuild. Not sure which feature pays back first? A short consulting engagement ranks them.
Start here
Mira suggests
Chat and voice assistants that answer from your knowledge base, book and route requests, and pass the hard cases to your team with the context attached.
Start here
Mira suggests
Retrieval-augmented generation turns contracts, manuals and policies into answers that cite the passage they came from, and respects who is allowed to see what.
Start here
Mira suggests
Models that forecast demand, score risk or read images and video, deployed with monitoring so you can see when they drift and retrain them.
Start here
Mira suggests
LLM, agent, ML and MLOps engineers who join your team by the month, or a dedicated team that owns the whole build. If an AI-built app has stalled, rescue it first.
Start here
What Mira AI builds
Every kind of AI, built by one team
From a single assistant to agents that run whole workflows. Each tile opens the service page with the details, the process and the pricing.
Assistants and copilotsWorkflow automationFull source code
Model choice per taskPrompt and output checksCost caps
Access rules keptCited answers
Tools and approvalsAudit trail
Human handoffMultilingual
DetectionInspection
ActualForecast
Back-testedMonitored for drift
Brand-safe output
Web, app, WhatsApp
PipelinesRegistryDrift alerts
AI development
ML, vision and data
Teams and rescue
AI by team
Where AI earns its keep inside a company
Pick a team to see the work AI takes off its plate, and the Miracuves service that builds it.
Sales
Sales teams use AI to spend their hours on the deals most likely to close.
- Lead scoring from CRM history
- Call and meeting summaries
- Proposal and email drafts
- A sales assistant on your data
Customer support
Support teams use AI to answer the repeat questions and keep people for the hard ones.
- Chat and WhatsApp assistants
- Voice agents for inbound calls
- Ticket triage and routing
- Help-center answers that cite sources
Operations
Operations teams use AI to take the reading, checking and re-typing out of daily work.
- Document extraction
- Workflow agents
- Demand and capacity forecasts
- Visual inspection
Finance
Finance teams use AI to check more, faster, and keep a person on every decision that moves money.
- Invoice and expense checks
- Fraud and anomaly signals
- Cash and revenue forecasts
- A reporting assistant
HR and hiring
HR teams use AI to answer staff faster, while every hiring decision stays with a person.
- Policy and handbook assistant
- Screening support
- Onboarding assistant
- AI skills for your own team
Marketing
Marketing teams use AI to produce more on-brand work and to show each visitor what fits.
- Content and product copy
- Avatar and explainer video
- Audience segments and propensity
- Search and recommendations
Product and engineering
Product teams use AI to ship features users notice, without rebuilding what already works.
- AI features inside your product
- Copilots for your users
- Model deployment and monitoring
- Rescue for AI-built code
Ready-made AI
Launch your own AI platform in 6 working days
Production-ready AI products with apps, admin and full source code, branded as yours.
AI by industry
Where AI pays off in your market
Turn the ring to your industry. Each one opens the page with the AI work that earns its keep there, and the platforms we have already built for it.
AI in healthcare
AI that takes admin off clinicians, with a person signing off every clinical output.
- Symptom intake and triage routing
- Consult notes drafted for doctor sign-off
- Patient messages sorted and answered
- No-show prediction for scheduling
AI in fintech and banking
Scores and signals that sit beside your ledger, with reasons on every decision.
- Fraud and anomaly signals on payments
- Credit scoring support with human review
- KYC document extraction
- A conversational banking assistant
AI in retail and e-commerce
Search that understands intent and a catalog that writes and tags itself.
- Intent search and recommendations
- A shopping assistant
- Product copy, tags and alt text
- Demand and inventory forecasting
AI in real estate
Faster matching and better leads, with fairness checks on every model.
- Buyer-to-listing matching
- Listing copy and photo tagging
- Lead scoring for agents
- Valuation support with ranges and comparables
AI in logistics
Dispatch and routing that suggest first, and automate once they have earned it.
- Dispatch and job assignment
- Route and stop sequencing
- ETA prediction
- Load-to-carrier matching
AI and data products
The AI products we ship as platforms: assistants, generative media and builder tools.
- AI assistant platforms
- Avatar and video generation
- Prompt-to-app builders
- AI with live experts
Four ways to get AI
The routes, side by side
The right route depends on what you have today. Here is how they differ on time, price, ownership and who runs it after launch.
How it works
From first call to live AI
- 01Talk to an AI engineer
First response under 2 hours, Mon-Sat 10:00-19:00 IST.
- 02Scope in writing, NDA first
What gets built, how it is checked and what it costs, agreed before any data is shared.
- 03Build in visible steps
Working demos as features land, tested against examples from your business.
- 04Launch
Ready-made platforms in 6 working days. Custom AI typically in 2-8 weeks.
- 05Hand over
You receive 100% of the source code, with support after launch.
How we build AI
Six rules on every AI project
Whether it is a ready-made platform, a single feature or a full product, the same rules apply.
- 01You own the code
100% of the source code is yours at handover, so your product never depends on us to keep running.
- 02Any model, no lock-in
GPT, Claude, Gemini, Llama and others are tools we pick per task. Open models can run on your own servers when data must stay in-house.
- 03Tested on your own data
Each AI feature is checked against real examples from your business before it reaches your users.
- 04People sign off where it matters
Credit, clinical and hiring decisions keep a human reviewer. The AI drafts, flags and ranks; a person decides.
- 05Running cost known early
We estimate what the models will cost each month before the build starts, so the provider bill holds no surprises.
- 06An NDA before your data
An NDA is signed before you share documents, recordings or customer data with the team.
The stack
The stack behind Mira AI
Tools we pick per task. None of them is a partner, and a model can be swapped later without rebuilding the product.
OpenAI GPTAnthropic ClaudeGoogle GeminiMeta LlamaMistralDeepSeek
pgvectorPineconeElasticsearchLlamaIndexHybrid searchRerankers
LangChainTool callingApproval stepsEvaluation setsAudit logs
WhisperDeepgramElevenLabsAzure AI SpeechTwilioLiveKit
OpenCVYOLOPyTorchOCR
scikit-learnXGBoostTensorFlowPyTorch
MLflowDockerKubernetesDrift monitoring
AWSGoogle CloudMicrosoft AzureYour own servers
Product names are trademarks of their owners and are listed only as tools Miracuves works with.
Proof
Built by a team shipping software since 2010
These clients added their own AI and model-driven features to platforms Miracuves built. Their words, unedited.
- 90+ready-made solutions
- 35+industries served
- 16+years shipping software
- 60days of guidance after launch
Conversational workflows, knowledge-base integration and the automation layer were already built. We connected our own data sources and tuned multilingual accuracy for support and lead qualification. The team now runs the whole AI operation from one place.
Miracuves's MXFlix + MXLearn combination gave us the player, the catalog, the user progress tracking, and the quiz/assignment layer. We added our path-recommender, our cohort-management module, and a custom certificate engine.
Player, CMS, subscriptions and the apps were all there. We brought the content pipeline, our recommendation logic and the parental-control layer. Live in weeks, not the six months a custom build would have cost, and we hit our Q1 window.
Clients who told their story
Answer 01
What does an AI development company actually build?
Three kinds of thing. First, features inside software: search that understands a question, summaries, drafting, classification and assistants that answer from your own data.
Second, systems that act: agents that read a request, check your records, take a step and ask a person to approve the ones that matter. Third, models that predict or see: forecasts, risk scores, and vision models that read images and video. Around all three sits the unglamorous work that decides whether AI is useful: cleaning and connecting data, choosing a model per task, building checks on what the model says, and monitoring it once real users arrive. A good AI partner also tells you when a plain rule or a report would do the job better, because not every problem needs a model.
Answer 02
Should I launch a ready-made AI platform or build custom AI?
Start from where the value sits. If your idea is a known product shape, such as an AI chat assistant, an avatar video studio or a prompt-to-app builder, a ready-made platform gets you live under your own brand in 6 working days at a fixed price, with the full source code.
You then spend your money on what makes you different: your market, your content, your customers. Choose custom AI when the value is in your own data or workflow, such as answers from your contracts, a forecast built on your sales history or an agent that works inside your systems. Custom work typically takes 2-8 weeks. Many projects combine the two: a ready-made base for the product, with custom AI features added on top.
Answer 03
What does an AI project cost, and what drives it?
Ready-made AI platforms have a fixed price, shown on each product page.
Custom AI is scoped and quoted in writing before work starts, and the quote moves with a few clear drivers. How much data work is needed before a model can use it. How many systems the AI reads from or writes to. How carefully the output has to be checked, since a draft a person reviews costs less to build than an automated decision. Whether the model is hosted or runs on your own servers. And the running cost: every model call has a price, so we estimate the monthly model bill before the build starts and design the feature to keep it predictable, with limits and caching where they help.
Answer 04
How long does it take to launch AI?
A ready-made AI platform goes live in 6 working days. Custom AI features and products typically take 2-8 weeks, and anything larger is scoped and quoted in writing before work starts.
The calendar is set less by coding than by three things around it: getting access to the right data and systems, agreeing what a good answer looks like, and testing on real examples from your business. The quickest projects pick one clear job for the AI first, prove it on your own data, and add the next feature once the first is earning its keep.
Answer 05
What data do I need before starting an AI project?
Less than most teams expect, but it has to be the right data.
An assistant that answers questions needs the documents it should answer from, with the access rules that decide who may see what. A forecast needs enough history of the thing you want to predict, recorded consistently. A vision model needs example images of what it should detect. Almost every project also needs a set of real questions or cases with the answers your team would give, because that becomes the test the AI must pass. If the data is scattered or messy, the first weeks go into connecting and cleaning it, which our data engineering work covers.
Answer 06
Which AI model should we use?
The one that fits the task, the budget and the rules on where your data may go, and that choice can change later.
Hosted models from OpenAI, Anthropic and Google are strong general choices and quick to start with. Open models such as Llama can run on servers you control when data must stay in-house. Smaller models are often good enough for classification or extraction and cost far less to run. We build behind a model gateway so a model can be swapped without rewriting the product, and we compare candidates on your own examples before deciding, rather than on a public benchmark.
Answer 07
How do you keep AI accurate and safe?
By treating accuracy as something measured, not hoped for. Each AI feature is tested against real examples from your business before it reaches users, and the same test runs again whenever a model or prompt changes.
Answers that should come from your documents cite the passage they used. Decisions that affect customers, such as credit, clinical or hiring calls, keep a person in the loop: the AI drafts, flags and ranks, and a person decides. Inputs and outputs are checked for personal data and off-topic requests, actions are logged, and an NDA is signed before you share any data with the team.
Answer 08
How do I evaluate an AI development partner?
Ask six questions. Who owns the code, the prompts and the model settings when the project ends?
Can they show how a feature will be tested on your data before launch? Will they tell you when AI is the wrong tool? Can they estimate the monthly model cost in writing? Are you locked into one model provider? And what happens after launch, from monitoring to support? Clear answers to those matter more than a demo. At Miracuves you own 100% of the source code, the model choice stays open, and the scope, the timeline and the cost are agreed in writing before work starts.
AI glossary
The words you will hear in every AI project
Twelve terms, explained in plain language, each linked to the work that uses it.
- LLMLarge language model
- A model trained on large amounts of text that can read, write, summarize and reason in plain language. GPT, Claude, Gemini and Llama are LLMs. On their own they know nothing about your business, which is why most products connect them to your data. LLM Development
- Generative AIAI that creates
- Models that produce new text, images, audio or video from a prompt. Useful for drafts, product copy, voiceovers and presenter videos, with a person checking what goes out under your name. Generative AI
- Fine-tuningTeaching a model your style
- Further training of an existing model on your own examples so it follows your format, tone or task more closely. Often retrieval does the job more cheaply, so fine-tuning is a choice to test, not a default. LLM Development
- HallucinationA confident wrong answer
- When a model states something that is not true as if it were. Reduced by grounding answers in your documents, asking the model to cite its source, and testing it on real questions before launch. RAG Development
- RAGRetrieval-augmented generation
- The model first finds the relevant passages in your documents, then answers from them and cites where each answer came from. It is how an assistant knows your policies, contracts and manuals. RAG Development
- EmbeddingsMeaning as numbers
- A way of turning text or images into lists of numbers so that similar meanings sit close together. They power search that understands a question even when it uses different words from the document. NLP Development
- Vector databaseSearch by meaning
- A database built to store embeddings and find the closest matches fast. Examples include pgvector and Pinecone. It is the memory behind most RAG systems. Data Engineering
- AI agentAI that takes steps
- A system that plans and carries out a multi-step task, such as reading a ticket, checking an order and issuing a refund, using tools you allow and asking for approval where it matters. AI Agent Development
- GuardrailsRules around the model
- Checks on what goes into and comes out of a model: blocking personal data, off-topic requests and unsafe actions, and forcing answers into the format your system expects. AI Integration
- Computer visionAI that sees
- Models that detect, count and inspect things in images and video, from damaged parcels and product labels to documents that need reading. Computer Vision
- MLOpsRunning models in production
- The pipelines and monitoring that ship a model, track its accuracy on live data and retrain it when it slips, the way DevOps does for ordinary software. MLOps
- Model driftWhen accuracy slips
- The slow loss of accuracy as real-world data moves away from the data a model learned on. Caught by monitoring and fixed by retraining on fresh examples. MLOps
AI insights
Field notes on building with AI
What we have learned shipping AI products, from our blog.
Visit the blog01What is Mira AI?
Mira AI is the name for all of the AI work Miracuves does: custom AI and LLM development, AI agents, machine learning and data services, AI for specific industries, AI engineers you can hire, and ready-made AI platforms. This page links every one of them, so you can start from the route that fits where you are today.
02Should I launch a ready-made AI platform or build custom AI?
Choose a ready-made platform when a proven product shape fits your idea, such as an AI chat assistant, an avatar video studio or an AI app builder. It launches under your brand in 6 working days at a fixed price. Choose custom AI when the value sits in your own data or workflow. Custom work typically takes 2-8 weeks, and larger scopes are quoted in writing first.
03How long does an AI project take?
A ready-made AI platform goes live in 6 working days. Custom AI features and products typically take 2-8 weeks, depending on the data, the systems involved and how the output has to be checked. Anything larger is scoped and quoted in writing before work starts.
04Do I own the code and the AI I pay for?
Yes. You receive 100% of the source code. Hosted models such as GPT, Claude or Gemini run under their provider's terms, and open models such as Llama can run on servers you control when your data must stay in-house.
05Can you add AI to an app I already have?
Yes. AI integration adds features such as search, summaries, drafting or an assistant to software you already run, without a rebuild. If your app was built with AI coding tools and has stalled, AI App Rescue audits it, fixes what is unsafe and finishes it.
06Which AI models do you work with?
We integrate models from OpenAI, Anthropic, Google and Meta, other open models, and speech tools for voice agents. The choice is made per task on quality, running cost and where the data is allowed to go. We are not tied to one provider, so a model can be swapped later.
07Is Mira AI a separate company?
No. Mira AI is how Miracuves presents its AI work: the same team, the same contracts and the same ownership terms as every other Miracuves project.
08Can the AI run on our own servers?
Yes. When your data must stay in-house, open models such as Llama can run on servers or a private cloud you control. Hosted models from OpenAI, Anthropic or Google are used under their terms when they suit the task better, and the choice is agreed in the written scope.
09Do you sign an NDA before we share data?
Yes. An NDA is signed before you share documents, recordings or customer data, and access to your systems is limited to what the build needs.
10What support do we get after launch?
Ready-made platforms include 60 days of support after launch. Support for custom AI work is set in the written scope, and ongoing improvement can run as a monthly retainer.
11Can you work alongside our in-house team?
Yes. AI engineers can join your team by the month, or we build a feature and hand it over with the code and documentation your team needs to run it.
12How do I get started?
Book a call with an AI engineer or send us your brief. We reply within 2 hours, Mon-Sat 10:00-19:00 IST, and sign an NDA before you share any data.
Mira AI
Tell Mira what you want to build
Type it in plain words. Mira points you to the right page, or an engineer maps it to the fastest route: ready-made, custom, or added to what you already run.
Talk to an AI engineer First response under 2 hours, Mon-Sat 10:00-19:00 IST
Trademarks. Miracuves is an independent software development company, not affiliated with, sponsored or endorsed by the owners of ChatGPT, DeepSeek, Gemini, Grok, HeyGen, Bolt.new, Apple or any model or tool named on this page. Names such as "ChatGPT Clone" describe the kind of software offered; every design and line of code is built by our own team. All trademarks belong to their owners. Legal notice and disclaimer
