Grounded answers with citations
Every response draws on retrieved passages and links back to sources users can verify in a snippet preview.
AI-Built ChatGPT Clone
ChatGPT Clone App Development
An AI-first build of the private AI assistant pattern: retrieval-augmented answers grounded in your data with citations, fine-tuning with versioned rollouts, and guardrails and token metering your team controls, deployable on-prem. Three synced panels ship white-label with 100% source code. The almost-ready AI build goes live in 6 working days; a custom build takes 3 to 4 weeks.
Launch-ready from
$2,799
- Live in 6 working days
- 60 days support
- Complete source codes
- Complete rebranding
- White-labeling
- 1 year free updates
Synced panels
Almost-ready AI launch
Custom build
Source-code ownership
Trusted by the brands we build for
AI credentials, stated plainly
Canonical facts behind every build.
- /01120+AI Products in Production98% model accuracy
- /022010Operating Since15+ years shipping apps
- /039,000+Projects Deliveredacross 35+ industries
- /043,900+Apps Publishedin 25+ countries
overview
What is a chatGPT clone app?
A ChatGPT clone app is a private AI assistant platform: users ask questions in a chat interface and receive streaming answers grounded in a knowledge base you control, with citations back to sources. Ours is built AI-first in the fullest sense, since retrieval quality, guardrails, and token metering are the product. It ships as a user app, a knowledge and fine-tuning console, and an admin panel, deployable to your cloud or on-prem.
The business problem it solves is controlled intelligence. Public assistants cannot see private documents and cannot be priced or governed by you, while an owned platform runs retrieval-augmented generation over your data, meters tokens per user and workspace, and enforces your content policies on every exchange.
The platform is a retrieval pipeline and a governance layer as much as a chat window: document parsing, chunking, embedding, and reranking decide answer quality, while filters, PII redaction, and citation-coverage reporting decide whether the enterprise trusts the answers.
Retrieval-augmented assistants over private data are now standard enterprise spending, and industry surveys repeatedly cite knowledge-grounded AI as the fastest-moving deployment pattern in regulated industries.
Main features of our ChatGPT-style assistant
Headline capabilities above the detailed panel breakdown below.
RAG over private data
Connector sync for drives, wikis, and CRMs, with tunable chunking and retrieval scoring in your console.
Fine-tuning with versioning
Tuning jobs from your conversation data, with evaluation scoring, A/B rollout, and rollback to any version.
Guardrails both sides
Input filters and PII redaction before generation, output filters and coverage checks after.
Token metering
Usage recorded per user, team, and workspace, with quotas, overage rules, and billing exports.
Multi-model routing
Requests route to the cheapest capable model, including self-hosted open weights on your own GPUs.
On-prem deployment
The full stack, retrieval included, runs inside your network for data that cannot leave it.
Evaluation harnesses
Regression scoring and citation-coverage reports so answer quality is measured, not assumed.
Who should launch a ChatGPT-style platform?
Every buyer on this list has knowledge a public assistant cannot reach, or margins a metered subscription is eating.
Document-heavy enterprises
Firms with large document estates that need grounded answers over proprietary knowledge, with governance compliance teams accept.
Support organizations
Automating answers from tickets and help centers, with citation coverage that shows exactly where answers come from.
Product companies
Embedding a grounded assistant into the software they sell, with their own API keys and margins.
Agencies and consultancies
Reselling white-label assistants to clients, each a standalone deployment under the client's brand.
Regulated and regional operators
Serving markets where data-residency rules rule out foreign platforms, with on-prem and private-cloud options.
Feature panels for an enterprise AI assistant
Retrieval quality, control, and cost visibility are the product, not the chat window.
User app
Where your users ask questions and get answers grounded in your data.
- ✓Chat with streaming responses and citations
- ✓RAG over your documents, wikis, and tickets
- ✓Source-linked answers with snippet previews
- ✓Conversation history with folders and search
- ✓Document upload with processing status
- ✓Prompt library and templates
- ✓Multi-model selection per conversation
- ✓Voice input and read-aloud
- ✓Feedback buttons that feed evaluation
Knowledge & Fine-tuning console
The panel that turns a generic assistant into your assistant.
- ✓Connector sync for drives, wikis, and CRMs
- ✓Chunking strategy and embedding model controls
- ✓Retrieval testing with relevance scoring
- ✓Custom model fine-tuning from conversation data
- ✓Fine-tune versioning with A/B rollout
- ✓System-prompt and persona management
- ✓Evaluation sets with regression scoring
- ✓Rollback to any model version
Admin, guardrails & metering panel
Governance and unit economics under one console.
- ✓Token metering per user, team, and workspace
- ✓Usage quotas, overage rules, and billing exports
- ✓Input and output content filters
- ✓PII redaction and data-retention policies
- ✓SSO, roles, and audit trails
- ✓Hallucination and citation-coverage reports
- ✓Model routing by cost and capability
- ✓On-prem or private-cloud deployment controls
The admin control plane: running the assistant as an operator
Four consoles, one view of the platform's usage, quality, and economics.
Knowledge and retrieval console
Document ingestion pipelines with parsing and chunking strategy, embedding and reranker selection as tunable settings, knowledge-base governance per workspace, and relevance scoring to test retrieval before users feel it.
Model and cost console
Model routing policies by cost and capability across commercial APIs and self-hosted weights, per-model credit pricing with a dynamic cost calculator, fine-tune versioning with rollback, and A/B rollout gates.
Metering and billing console
Token metering per user, team, and workspace, usage quotas with overage rules and billing exports, subscription plan configuration with free trials, and payment-gateway administration.
Guardrails and audit console
Input and output content filters, PII redaction and data-retention policies, hallucination and citation-coverage reports, SSO with roles and audit trails, and user and interaction analytics across the deployment.
How an AI assistant works: from question to cited answer
The request lifecycle that turns private documents into grounded answers.
- 01
Ask
A user types or speaks a question, selects a workspace, and the request enters the pipeline with input filters and PII redaction applied first.
✓ step 01 complete - 02
Retrieve
The question is embedded and matched against the indexed knowledge base, with a reranker pulling the most relevant passages.
✓ step 02 complete - 03
Generate
The selected model receives the question plus retrieved context and streams an answer that carries citations back to the sources.
✓ step 03 complete - 04
Verify and cite
The user checks snippet previews against the answer, and citation-coverage reporting records how well the response is grounded.
✓ step 04 complete - 05
Meter and bill
Token usage records per user, team, and workspace, quotas enforce plan limits, and overage rules feed the billing export.
✓ step 05 complete - 06
Learn and improve
Feedback buttons and evaluation sets score answer quality, fine-tuning runs on conversation data, and new model versions roll out with A/B gating.
✓ step 06 complete
AI inside the platform
Five model-driven systems working the same request stream. Each ships configured and measurable, because on this platform the AI is the product.
RAG retrieval pipeline
Parses, chunks, embeds, and reranks your documents so every answer is grounded in retrieved passages with citations.
Reads
Document corpus, query embeddings, relevance scores, chunk metadata
Changes
Answers your enterprise can verify, not vibes
Fine-tuning and versioning
Runs tuning jobs from your conversation data with evaluation scoring and A/B rollout, so improvements ship on evidence.
Reads
Conversation data, evaluation sets, feedback signals
Changes
A model that gets measurably better at your domain
Guardrails engine
Applies input filters and PII redaction before generation and output filters and coverage checks after, on every exchange.
Reads
Prompt and response content, policy rules, PII patterns
Changes
Deployments compliance teams approve
Multi-model routing
Sends each request to the cheapest capable model, including self-hosted open weights, under policies your team controls.
Reads
Request complexity, model capabilities, cost tables, quotas
Changes
Lower per-answer cost at the same quality bar
Evaluation harnesses
Runs regression scoring and citation-coverage reports so answer quality is tracked release over release.
Reads
Evaluation sets, user feedback, coverage metrics
Changes
Quality measured in numbers, not anecdotes
platform concepts
Concepts that run the assistant
Retrieval-augmented generation is the operating principle: the model never answers from memory about your data, it answers from passages retrieved at question time. Document parsing, chunking strategy, embedding choice, and reranking decide answer quality, which is why the knowledge console exposes each of those as tunable settings with relevance scoring for testing.
Guardrails run on both sides of every exchange: input filters and redaction before generation, output filters and citation-coverage checks after. Token metering is the unit-economics concept, recording cost at the prompt level so per-seat and usage pricing both stay auditable.
Model routing ties it together, sending each request to the cheapest capable model, including self-hosted open weights on-prem, under policies your team controls. Versioning means every fine-tune and routing change is rollback-able.
Feature groups that decide whether enterprises adopt the assistant
Four groups, each mapped to the part of the adoption promise it protects.
Grounded answers
A RAG pipeline that parses, chunks, embeds, and reranks your document corpus, with citations and snippet previews on every answer and relevance scoring to test retrieval before users ever see it.
Multi-model gateway
Model routing by cost and capability across commercial APIs and self-hosted open weights, with per-model credit pricing and a dynamic cost calculator, so quality targets and unit economics are both deliberate choices.
Workspace productivity
Conversations organized by team and workspace, custom prompt templates per department, document uploads into governed knowledge bases, and shareable assistant outputs from code to marketing copy.
Governance and metering
Token metering per user, team, and workspace with quotas and overage rules, PII redaction and content filters on both sides of every exchange, hallucination and citation-coverage reports, and audit trails compliance can read.
revenue model
How your AI platform earns
Monetization is admin-configurable: per-seat subscriptions with token quotas, usage-based overage billing, workspace plans with premium models and higher context limits, and API access resold to integrators under your terms. Token metering makes every line auditable down to the prompt.
Because 100% of the source code is yours, the platform can run against your own API keys or self-hosted open-weight models, which is what makes on-prem and private-cloud deployments possible for regulated teams.
insight
The market numbers behind the AI assistant opportunity
Generative-AI software spending is projected to grow at triple-digit percentage rates through the decade according to widely published industry analyses, with enterprise assistants the fastest-moving slice. Buyers have learned the lesson of the first wave: the durable value is not the model, it is the grounded data, the guardrails, and the metered economics around it.
The wedge for a vertical operator is domain depth: an assistant grounded in one industry's documents, priced with credits that reflect real per-model costs, and deployable on-prem for regulated teams beats a general tool that can promise none of that. The revenue table below quantifies the lines.
revenue model
How your AI platform earns
Monetization is admin-configurable: per-seat subscriptions with token quotas, usage-based overage billing, workspace plans with premium models and higher context limits, and API access resold to integrators under your terms. Token metering makes every line auditable down to the prompt.
Because 100% of the source code is yours, the platform can run against your own API keys or self-hosted open-weight models, which is what makes on-prem and private-cloud deployments possible for regulated teams.
Revenue lines, typical ranges, and what sets each ceiling
Six monetization lines across the platform, quantified against published industry benchmarks. Every rate is admin-configurable; the figures here are modelling inputs, not projections of your platform's earnings.
| Criteria | Revenue line | How it is charged | Typical range | What sets the ceiling |
|---|---|---|---|---|
| Per-seat subscriptions | Monthly tiers with token quotas and feature gates | AI assistant seats commonly $20 to $60+ per user per month | Answer trust. Seats renew where citations hold up under scrutiny | |
| Usage overage billing | Metered token billing beyond plan quotas | Priced per million tokens with model-tier multipliers | Unit economics. Overage earns while metering stays auditable to the prompt | |
| Workspace and team plans | Team tiers with shared knowledge bases and admin controls | Team plans commonly $25 to $50+ per user per month | Collaboration value. Team plans earn where shared workspaces compound | |
| Premium model access | Higher-tier models and longer context sold as upgrades | Premium model access commonly 2x to 10x base credit cost | Capability fit. Premium access earns where routing keeps base costs honest | |
| API resale | Metered API access resold to integrators under your terms | API plans commonly $100 to $5,000+ per month by volume | Ecosystem gravity. API earns while your grounding and guardrails stay the differentiator | |
| On-prem and private-cloud licenses | Annual licenses for self-hosted deployments on open weights | Enterprise licenses commonly $50,000+ per year by scale | Data control. Licenses earn where regulated buyers cannot ship data out |
Every build ships with
Standard inclusions on every engagement, almost-ready or custom. Nine things you never have to ask for.
100% source code, full IP
User app, knowledge console, and admin panel transfer fully on delivery, with documentation and deployment scripts.
White-label branding
Your brand across every surface, deployed to your cloud or on-prem infrastructure under your accounts.
Store and web delivery
Mobile apps submitted to Apple and Google and the web app deployed to your domain, with production monitoring on.
Payment integrations
Stripe, PayPal, and regional gateways configured for seat plans, token overages, and workspace billing.
AI stack Pre-configured
RAG pipeline, guardrails, token metering, and model routing arrive configured and evaluation-tested, not as raw APIs.
Multi-language and Multi-currency
Interface languages, caption and answer languages, and currency setups configured during the launch window.
Flexible deployment
Cloud, private-cloud, or on-prem with open-weight model support for data that cannot leave your network.
Admin analytics and reports
Usage, cost, quality, and coverage dashboards plus CSV exports so your team starts informed on day one.
Post-launch support and updates
Included support window plus product updates covering bug fixes, model upgrades, and version releases.
The stack behind grounded answers
Chosen for retrieval quality, cost control, and deployment flexibility.
LLM layer
Multi-model routing across OpenAI, Anthropic, and open-weight models served via vLLM for on-prem.
RAG pipeline
Document parsing, semantic chunking, vector search with reranking, and citation generation.
Fine-Tuning
LoRA-based tuning jobs with evaluation harnesses and versioned model registries.
Platform
Next.js web and Flutter or React Native clients, Node.js or Python services, Postgres with pgvector.
Integrations
Drive, wiki, and CRM connectors, payment gateways, SSO identity, and analytics ship as configured integrations.
Security and compliance
Role-based access, append-only audit logs, PII redaction, retention policies, and on-prem deployment controls come standard for regulated buyers.
Your launch timeline: live in 6 working days
The almost-ready AI build, day by day. Store review can add a short buffer outside our control.
- 01
Days 1-2: scoping and branding
Knowledge sources, deployment surface, and pricing model locked with a written scope, and your identity applied across all panels.
✓ step 01 complete - 02
Days 3-4: configuration and integration
Connectors, guardrail policy, token plans, and model routing configured and tested against your live accounts and documents.
✓ step 02 complete - 03
Days 5-6: knowledge onboarding and pilot
Your documents parsed, chunked, and indexed, then the ask, retrieve, cite, and meter flows tested against real evaluation questions.
✓ step 03 complete - 04
Days 7-9: store review and go live
App and web delivery with review buffers accounted for, production monitoring on, and full launch with post-launch support and product updates included.
✓ step 04 complete
Almost-ready AI launch or custom build for your assistant
Both tracks ship the same retrieval core. The difference is time, price structure, and how much you want to change.
| Criteria | Almost-Ready AI Launch | Custom Build |
|---|---|---|
| Time to live | 6 working days from branding handoff | 3 to 4 weeks, scoped before work starts |
| Pricing | Fixed written quote after a short scoping call, no hourly meter | Estimated per module after the discovery call |
| Source code and IP | 100% ownership on delivery | 100% ownership on delivery |
| AI layer | RAG, guardrails, and metering pre-configured with evaluation harnesses | Retrieval and models designed around your data and workflows |
| Deployment | Cloud or private cloud out of the box | Full on-prem with self-hosted open weights to your spec |
| Best for | Operators who want to launch this quarter | Teams with bespoke workflows or a unique model |
| After launch | Post-launch support and product updates included | Post-launch support and product updates included |
$ moxace launch --nowAsk our assistant something private before you commit.
Book a walkthrough and we will demo retrieval, citations, guardrails, and metering on a live build, then send the written scope.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
What moves your quote for an AI assistant platform
No two AI assistant platforms price the same. These are the factors that shape the written quote you receive after scoping.
Scope of customization
The standard build covers chat, workspaces, and the retrieval core. Custom agent workflows, audit tooling, or industry-specific model behavior are scoped work.
Zones, cities, and markets
Multi-language interfaces, data-residency deployment options, and per-market content policy surfaces are configured at launch.
Integrations
Document stores, vector databases, single sign-on, and API gateways for tool calling are wired during the build.
AI tuning depth
The assistant arrives pre-tuned for enterprise question answering. Fine-tuning models on your knowledge base and usage patterns is a separately scoped workstream.
Add-on modules that deepen the assistant
Extend the AI platform once the core ask-and-cite loop is running.
Multimodal generation suite
Image, video, voice, and avatar generation attached to the same credit system, so one wallet covers text, media, and speech workloads with per-model pricing.
Agent workflows
Multi-step tool-using agents that act on your systems, from drafting tickets to reconciling records, governed by the same guardrails and metering as chat.
API and webhook builder
A self-serve surface where integrators wire the assistant into CRM, e-commerce, and support tools under metered API keys and your terms.
Evaluation and feedback studio
A workspace where domain experts curate evaluation sets, score regressions, and run A/B rollouts, making answer quality a managed release process.
Extension marketplace
A catalog of paid capability packs (generation modes, connectors, templates) your team can publish, extending revenue without core releases.
On-prem deployment kit
Packaging, model-serving templates, and update channels for private-cloud and air-gapped deployments on open weights, licensed annually.
/22
Why build your AI assistant here
Differentiators that matter to buyers who have priced the alternatives.
AI-first track record
120+ AI products in production and 98% model accuracy across deployments, not a chat plugin bolted onto an agency.
Grounded, not glib
RAG with citations and evaluation harnesses ships on day one, so accuracy is measured rather than assumed.
Deploy where policy demands
On-prem and private-cloud options with open-weight model support for data that cannot leave your network.
Ownership economics
Full source code, your own API keys, and no per-token platform levy: the margins are yours. Broader scope sits on our AI chatbot development page.
Proof from platforms we have shipped
Canonical deployments from our own portfolio. Metrics are reported from live platforms, not projections.
PragPlay
A high-concurrency real-time platform from the same engineering system, proving sustained-load reliability at scale.
500K+
Players
99.9%
Uptime
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
Marvel Studios
A high-traffic media platform our team ships and operates at franchise scale, the same bar every build is held to.
10M+
Streams
65%
Completion Rate
ChatGPT clone FAQs
Security and integrity
Tap a card to see how the platform defends its answers and its economics.
Answers Are Grounded or Flagged
Generation runs on retrieved passages with citations attached, and citation-coverage reporting scores how well every response is grounded, so an unsupported answer is a measurable event, not a vibe.
Guardrails Wrap Every Exchange
Input filters and PII redaction run before generation, output filters and coverage checks after, on every request. Policy is enforced in the pipeline, not left to the model's mood.
Every Token Is Accounted
Metering records cost at the prompt level per user, team, and workspace, and quotas plus overage rules feed billing exports, so unit economics audit down to a single question.
Routing Decisions Are Yours
Model routing sends each request to the cheapest capable model, including self-hosted open weights, under policies your team controls, with every change versioned and rollback-able.
Models Improve on Evidence
Fine-tuning runs with evaluation scoring and A/B rollout against curated evaluation sets, so a model change ships when the numbers move, not when a demo looks good.
Data Never Leaves Without Consent
Deployment controls support on-prem and private-cloud operation on open weights, with retention policies and audit trails, so regulated teams keep data residency without losing capability.
Frequently asked questions
the process
What to expect when you commission this build
A discovery session maps your knowledge sources, deployment surface, and pricing model, and ends in a fixed written quote. The almost-ready AI build configures branding, connectors, guardrail policy, and token plans for a launch in 6 working days; fine-tuning and on-prem work run as scoped engineering phases with evaluation gates.
Handover includes console training, 60 days of support, and 12 months of updates. Expect the first 90 days to be a quality-tuning period: measuring citation coverage on real questions, refining chunking and retrieval where answers miss, and iterating guardrail thresholds with your compliance reviewers. Assistants earn organizational trust in that window, and the evaluation tooling exists precisely so you can see it happening.
Live in action
See It Live Before You Talk to Us
Open the web app, sign in with the printed demo credentials, or install the Android build — no signup, no sales call. What you see is what you launch.
$ moxace launch --nowPut a private AI assistant to work.
Demo, scope, and a fixed quote in one working session with the AI engineering team.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
Independent Build
Moxace is an independent software development company. We are not affiliated with, sponsored by, or endorsed by ChatGPT.
Why This Name
The phrase “ChatGPT clone” is used descriptively. It is how the software industry refers to building a platform with similar functionality, and how buyers search for one.
Who Built This
The design and codebase are built entirely by our in-house team. Nothing is taken from the ChatGPT website, applications, or brand assets.
Trademarks
ChatGPT and all third-party names and marks remain the property of their respective owners, referenced here only to describe the category of software we build.
Read the full legal notice on our disclaimer page.