Multimodal input and output
Text, images, documents, and voice in one conversation, with outputs matched to the task: drafts, analysis, images, or structured data.
AI-Built Gemini Clone
Gemini Clone App Development
An AI-first build of the multimodal assistant pattern: one interface across text, image, code, and documents, model routing that picks the right engine per task, and workspace integration that makes the assistant part of how work actually happens. Four synced panels ship white-label with 100% source code, owned by you from day one. 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
AI engineers
Almost-ready AI launch
Custom build
Source-code ownership
Trusted by the brands we build for
Moxace AI platforms by the numbers
Canonical facts, not promises.
- /012010Operating Since15+ years shipping apps
- /029,000+Projects Deliveredacross 35+ industries
- /033,900+Apps Publishedin 25+ countries
- /04120+AI Products in Production98% model accuracy
overview
What is a Gemini-style AI assistant platform?
A Gemini-style platform is a branded multimodal AI assistant: users draft, analyze, generate images, and reason over documents from one interface, with models routed per task, conversation history that persists meaningfully, and integrations that let the assistant act inside the user's actual workspace rather than a demo sandbox.
The label describes the category benchmark, not the code. You launch under your own brand with your own model strategy: frontier cloud APIs, open-weight models you host, or a routed mix, all behind an interface and data policy you control.
A complete build includes the assistant app for web and mobile with multimodal input, a model routing layer, workspace integrations for documents, email, and calendars where permissions permit, an admin console for models, quotas, and policy, and the evaluation tooling that keeps quality measurable.
Market context, hedged: AI assistants are the most crowded consumer-AI category, which is exactly why the winning entries are vertical and regional: one industry's documents, one language's nuance, one company's workflow. We make no claims about market size; the practical signal is whether you hold an audience and a workflow a generic assistant does not fit.
Main features of our Gemini-style platform
The headline capabilities, at product level.
Intelligent model routing
Requests route to the right engine per task, cost, and latency profile, so fast questions get fast models and hard ones get frontier ones.
Conversation memory and search
Threads persist with summaries and full-history search, so the assistant compounds usefulness instead of restarting every session.
Document and data analysis
Upload documents, spreadsheets, and images and ask questions across them, with citations pointing back to the source material.
Code and data generation
Code generation, explanation, and review with execution sandboxes, plus charts and tables generated from user data.
Workspace integrations
Connect documents, email, and calendars where permissions permit, so the assistant works on the user's actual material under explicit scopes.
Enterprise controls
SSO, role-based access, usage quotas, data-retention policy, and audit logs, the layer that separates an enterprise product from a demo.
Quality evaluation harness
Continuous evaluation sets and feedback loops so model and prompt changes are measured before they ship, not discovered by users.
Who should launch a Gemini-style platform?
What these buyers share: an audience and a workflow a generic assistant does not fit. If you bring the users, the assistant platform is the part we handle.
Vertical SaaS founders
Embedding a branded assistant across your industry product, with the models routed to your domain's tasks. The AI core ships pre-tuned.
Enterprises building internal AI
Giving staff a governed assistant with your data policy, SSO, and audit, instead of a consumer tool your security team cannot see into.
Education and training providers
Tutoring and study assistants grounded in your curriculum, with controls appropriate to your learners.
Language and regional operators
Serving a language and cultural context the global assistants handle generically, with models routed to what your users actually need.
Agencies and resellers
Deploying branded assistant platforms for enterprise clients, every deployment a standalone product on 100% source code.
Feature panels of your AI assistant platform
Four products operating as one system. Every panel ships in every build.
Assistant app
The interface users live in.
- ✓Multimodal chat with file uploads
- ✓Image generation and editing
- ✓Document Q&A with citations
- ✓Code blocks with execution sandbox
- ✓Conversation threads and search
- ✓Voice input and output
- ✓Draft and rewrite tools
- ✓Sharing and export
- ✓Mobile and web parity
- ✓Feedback capture on answers
Model routing layer
The engine selection underneath.
- ✓Per-task model configuration
- ✓Cost and latency budgets
- ✓Open-weight model hosting
- ✓Fallback chains on provider failures
- ✓Prompt and persona templates
- ✓Rate limiting and quotas
- ✓Response caching
- ✓Versioned model registry
Admin console
Govern the assistant fleet.
- ✓User and seat management
- ✓SSO and role-based access
- ✓Usage and cost dashboards
- ✓Data-retention policy controls
- ✓Workspace integration scopes
- ✓Abuse and safety monitoring
- ✓Evaluation quality reports
- ✓Audit logs and exports
Evaluation suite
Keep quality measurable.
- ✓Curated evaluation sets
- ✓Regression testing on prompt changes
- ✓Human review queues
- ✓A/B testing framework
- ✓Cost-per-quality reporting
- ✓Safety and red-team checks
- ✓Model comparison dashboards
- ✓Feedback-driven tuning
The admin control plane: running the assistant fleet as an operator
Four consoles, one view of models, users, and quality.
Model console
Per-task model configuration with cost and latency budgets, open-weight model hosting, fallback chains, and a versioned model registry.
Enterprise console
User and seat management, SSO and role-based access, workspace integration scopes, and data-retention policy controls per tenant.
Quality console
Evaluation quality reports, human review queues, A/B testing oversight, and safety and red-team checks feeding the tuning loop.
Money and safety console
Usage and cost dashboards, abuse and safety monitoring, quota and price configuration, and audit logs and exports.
How an assistant request runs
From prompt to grounded answer, end to end.
- 01
Prompt in
The user types, speaks, or uploads; the request arrives with attachments, conversation context, and permission scopes attached.
✓ step 01 complete - 02
Route
The routing layer classifies the task and picks the engine: fast model for simple drafts, frontier model for reasoning, image model for generation.
✓ step 02 complete - 03
Ground
Where the task needs the user's material, retrieval pulls from permitted documents and workspace sources, with sources tracked for citation.
✓ step 03 complete - 04
Generate
The model produces the response; code runs in sandboxes, images render inline, and drafts stream as they form.
✓ step 04 complete - 05
Review and cite
The answer presents with citations and confidence signals, and the user accepts, edits, or flags it, feeding the evaluation loop.
✓ step 05 complete - 06
Persist and learn
Threads persist with summaries, feedback and quality signals flow to the evaluation suite, and routing policy sharpens from real usage.
✓ step 06 complete
AI inside the platform
Five model-driven systems working the same request stream. Each ships pre-tuned for assistant workloads and sharpens as your data compounds.
Task-aware model routing
Classifies each request and routes to the engine that fits its difficulty, latency needs, and cost budget, per your model strategy.
Reads
Request content, task type, cost budgets, provider health
Changes
Frontier quality where it matters, fast and cheap where it does not
Grounded retrieval
Pulls relevant material from permitted documents and workspace sources so answers cite the user's own material.
Reads
Document index, permission scopes, relevance signals
Changes
Answers grounded in real material instead of plausible guesses
Multimodal understanding
Handles images, documents, and charts as first-class inputs, extracting structure the conversation can reason over.
Reads
Uploaded media, document structure, prior context
Changes
One assistant for the whole workflow, not a text-only toy
Safety and abuse filtering
Guards inputs and outputs against policy violations, prompt injection, and data leakage per your governance rules.
Reads
Request and response content, injection patterns, policy rules
Changes
An assistant your security team signs off on
Quality evaluation loop
Scores responses against evaluation sets and user feedback, so routing, prompts, and models improve on evidence.
Reads
Acceptance rates, corrections, evaluation scores, cost data
Changes
Quality that measurably improves release over release
platform concepts
Concepts that make an assistant platform good
The first concept is routing economics. Not every prompt needs the most expensive model, and routing that classifies task difficulty, latency needs, and budget per request is what keeps quality high while unit costs stay predictable. Routing policy is admin-configurable, so finance and product both get a dial.
The second is grounding. An assistant that answers only from model weights is a toy; one that reasons over the user's documents, with citations and permission scopes, is a product. Retrieval, scopes, and citation rendering are therefore core architecture rather than a plugin.
The third is evaluation. Model and prompt changes without measurement are vibes; a curated evaluation set, regression testing, and feedback loops make quality a number that moves. That discipline is what separates a platform that improves quarterly from one that quietly regresses.
How your AI assistant platform earns
Subscription tiers gating capability: model access, usage quotas, workspace integrations, and team features.
Enterprise licensing with SSO, private deployment options, audit exports, and support SLAs.
Usage metering for premium model minutes and generation credits where you enable them.
Vertical marketplace potential: domain packs, prompt libraries, and integrations sold to power users.
Every quota and price lives in the admin console, so you can reprice as model costs shift without a code release.
Feature groups that decide whether the assistant improves quarterly
Four groups, each mapped to the part of the assistant promise it protects.
Routing economics
Routing that classifies task difficulty, latency needs, and budget per request, with fallback chains on provider failures, versioned model registries, and response caching, keeping quality high while unit costs stay predictable.
Grounded answers
Retrieval over permitted documents and workspace sources with citations and permission scopes as core architecture rather than a plugin, so the assistant reasons over the user's material instead of guessing.
Enterprise controls
SSO, role-based access, usage quotas, data-retention policy, workspace integration scopes, and audit logs, the layer that separates an enterprise product from a demo.
The evaluation discipline
Curated evaluation sets, regression testing on prompt changes, human review queues, A/B testing, and cost-per-quality reporting, making quality a number that moves rather than vibes.
insight
The market numbers behind the assistant platform opportunity
AI assistant platforms keep compounding toward the hundred-billion-dollar mark globally by 2030 on widely published forecasts, with vertical and enterprise deployments the segment where generic assistants fail and grounded, governed builds win the contract.
The engineering wedge is grounding and evaluation: an assistant that cites the user's own material with permission scopes, and a quality harness that catches regressions before users do, are what renew enterprise seats. The revenue table below quantifies the lines.
Revenue lines, typical ranges, and what sets each ceiling
Six monetization lines across the platform, quantified against published industry benchmarks. Every quota 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 |
|---|---|---|---|---|
| Subscription tiers | Model access, quotas, integrations, teams | Plans commonly $15 to $200+ per month | Answer quality. Tiers hold while output stays trusted | |
| Enterprise licensing | SSO, private deployment, SLAs, audits | Contracts commonly $10,000 to $500,000+ per year | Governance trust. Contracts hold when the audit passes | |
| Usage metering | Premium model minutes and generation credits | Metering commonly $5 to $200+ per month per heavy user | Routing discipline. Metering earns while budgets stay predictable | |
| Vertical marketplace | Domain packs, prompt libraries, integrations | Shares commonly 20% to 50% of sale value | Ecosystem supply. Revenue scales while the SDK stays good | |
| Seat expansion | Per-seat pricing as teams grow | Seats commonly $10 to $60+ per user per month | Daily habit. Seats grow while the assistant stays in workflow | |
| White-label deployments | Branded platforms for agencies | Licenses commonly $10,000 to $100,000+ | Reseller demand. Deals close while per-tenant config stays cheap |
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
The complete unencrypted codebase transfers to you, with documentation and deployment scripts.
White-label branding
Your logo, colors, and store accounts across all four panels, plus branded user communications.
Store submission and launch
App store and Play Console submissions handled end to end, with production monitoring switched on.
Model integration
Your chosen cloud providers and open-weight models configured, with routing, budgets, and fallbacks wired and tested.
AI systems Pre-tuned
Routing, retrieval, and safety models arrive configured for assistant workloads, not as raw APIs.
Multi-language support
Interface languages and multilingual model coverage configured for your markets during the launch window.
Scalable infrastructure
Containerized services with queue-backed inference and autoscaling, so usage spikes degrade instead of failing.
Admin analytics and reports
Usage, cost, and quality 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, OS compatibility, and model version upgrades.
Technology inside the platform
Routing and grounding are where assistants win or fail. We engineer them first.
Model orchestration
A versioned model registry with routing policies, fallback chains, and cost budgets, so your model strategy is configuration rather than a rewrite.
Retrieval and grounding
A vector and keyword retrieval layer over permitted sources with citation tracking, so answers point back to material users can verify.
Sandboxed execution
Code and data tasks run in isolated sandboxes with resource limits, so generated code can be useful without being dangerous.
Infrastructure
Containerized services on AWS or GCP with queue-backed inference. For the conversational-only pattern, see our ChatGPT-style assistant.
Integrations
Document stores, calendars, email, and identity systems ship as permission-scoped integrations, so the assistant works on real material under explicit user grants.
Security and compliance
SSO, role-based access, data-retention policies, audit logs, and encrypted traffic come standard, so the enterprise procurement conversation has answers.
Your launch timeline: live in 6 working days
The almost-ready AI build, day by day. Store review times can add a short buffer outside our control.
- 01
Days 1-2: scoping and branding
Model strategy, tiers, and integration scopes locked with a written scope, and your identity applied across all four panels.
✓ step 01 complete - 02
Days 3-4: model and policy configuration
Model providers and budgets configured, routing and fallbacks tested, and data-retention policies set per your requirements.
✓ step 02 complete - 03
Days 5-6: grounding and pilot
Document sources indexed under permission scopes, evaluation sets baselined, and a pilot cohort runs real workloads end to end.
✓ step 03 complete - 04
Days 7-9: store review and go live
App store submissions with review buffers accounted for, production monitoring on, and full launch with post-launch support included.
✓ step 04 complete
Almost-ready AI launch or custom build: the honest comparison
Both tracks ship the same assistant 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 | Routing, grounding, and safety models pre-tuned | Model strategy and routing designed around your workloads |
| Multimodality | Text, image, document, and code flows included | Plus custom modalities and domain tooling as scoped |
| Enterprise controls | SSO, quotas, and audit logging included | Governance designed to your compliance regime |
| Best for | Teams who want to ship this quarter | Products with bespoke workflows or strict regimes |
$ moxace launch --nowTest the assistant before you commit.
Book a walkthrough and we will demo multimodal requests, grounding, and the admin console, 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
No two launches price the same. These are the factors that shape the written quote you receive after scoping.
Scope of customization
The almost-ready AI build covers the proven feature set. Custom modalities, domain tooling, or bespoke integrations add scoped estimate work.
Model strategy
Cloud APIs, hosted open-weight models, or a routed mix each carry different infrastructure and licensing work, all estimated in writing up front.
Integrations
Workspace systems, identity providers, and document stores are wired during the build, and each integration is estimated individually.
AI tuning depth
Out of the box, systems are pre-tuned for assistant workloads. Fine-tuning models on your domain data is an optional, separately scoped workstream.
Add-on modules that deepen the platform
Extend the Gemini-style build once the core assistant loop is running.
Agent workflows pack
Multi-step tool-using agents with deterministic step logs, extending chat into automated work over workspace data.
Image and video generation
Generation and editing with entitlement-gated models and metered credits, the creative layer users upgrade for.
Team collaboration mode
Shared threads, prompt libraries, and role-scoped knowledge, turning individual assistants into team infrastructure.
Vertical domain packs
Packaged retrieval sources, prompts, and evaluation sets per industry, sold in the marketplace you cultivate.
Compliance pack
Data residency configuration, retention policies, and audit exports mapped to regulated buyers, sold as an annual add-on.
White-label reseller licensing
Deploy the platform for agencies under their own identity, with per-tenant models, quotas, and integration scopes.
/21
Why teams pick our Gemini-style build
Assistant product craft with production AI engineering.
Routing-first economics
Task-aware routing and cost budgets are core architecture, because an assistant with uncontrolled inference costs is not a business.
120+ AI engineers
A dedicated AI practice that has shipped 120+ AI products in production, applied to your roadmap from day one.
Full ownership
One hundred percent of the source code transfers to you. No per-request royalties on your own users, no black-box dependencies.
Adjacent products
Pair with our ChatGPT-style assistant for the text-first pattern, on the same engineering system.
Proof from platforms we have shipped
Canonical deployments from our own portfolio. Metrics are reported from live platforms, not projections.
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
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
Frequently asked questions
Security and integrity
Tap a card to see how the platform defends its answers.
Grounding Is Architecture, Not a Plugin
Retrieval over permitted documents and workspace sources runs with citations and permission scopes as core architecture, so the assistant reasons over real material instead of guessing.
Routing Has a Budget
Task difficulty, latency needs, and budget classify each request, with fallback chains on provider failures, so quality stays high while unit costs stay predictable.
Changes Meet Evaluation First
Curated evaluation sets, regression testing, and A/B testing gate model and prompt changes, so quality is a number that moves rather than vibes.
Permissions Scope Every Retrieval
Workspace integration scopes limit what the assistant can reach per user and role, so enterprise data policy is enforced by the platform, not promised.
Abuse Meets Monitoring
Abuse and safety monitoring watches usage patterns with quotas and rate limits, so one heavy tenant cannot degrade the fleet.
Every Quota Is a Dial
Model access, usage quotas, and prices configure from the admin console, so repricing as model costs shift takes no code release.
Frequently asked questions
the process
What to expect when building with us
The engagement starts with a discovery call and ends with a written scope and quote before any work begins. On the almost-ready AI path you hand over branding assets and your model preferences, we configure providers, routing, and grounding scopes, and go live in 6 working days. Custom modules are scoped as sprints and quoted in writing first, so there are no surprise invoices.
After launch you have post-launch support and product updates included, including model version upgrades as the provider landscape shifts. Expect the first 90 days to be about quality: watching the evaluation dashboards, adding the integrations your users actually request, and tuning routing as real usage patterns emerge. The platform handles orchestration and grounding; the workflow you point it at is what makes it indispensable.
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.
AI SaaS Web & Admin
- AI assistant chat
- Text-to-speech
- Templates
- File chat
- MXSeek LLM deployment (http://157.180.20.222:18480/)
User: [email protected] | User_321
Admin: https://mxai.mimeld.com/login
$ moxace launch --nowShip your AI assistant this month.
Book a free consultation and get a written scope and price.
- 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 Gemini.
Why This Name
The phrase “Gemini 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 Gemini website, applications, or brand assets.
Trademarks
Gemini 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.