Multimodal input
Images, PDFs, spreadsheets, and voice enter the same pipeline, normalized into retrieval-friendly representations.
AI-Built Gemini Clone
Build a Gemini AI Clone App
An AI-first build of the multimodal assistant: images, documents, and voice entering one grounded pipeline, RAG over private data with per-claim citations, and token metering weighted by modality. Three synced panels ship white-label with 100% source code, on-prem optional. 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
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Canonical Moxace facts behind every AI platform build.
- /01120+AI Products in Productionshipped for clients worldwide
- /0298%Model Accuracyacross deployed systems
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overview
What is a Gemini-style multimodal assistant?
A multimodal AI assistant understands text, images, documents, and voice in one conversation: users can photograph a chart, upload a contract, or speak a question and get a grounded, cited answer. The Gemini-style model combines multi-input understanding with retrieval over private data and organization-wide deployment.
The purpose is to remove the keyboard as a bottleneck: much of what people need help with arrives as images and files, not typed prompts. Operators launch multimodal assistants because they serve workplace reality, and because workspace plans, token metering, and API access monetize usage across consumers, teams, and developers.
A complete build includes the user assistant app, a workspace console for teams, and an admin panel over multimodal ingestion, a RAG pipeline with per-claim citations, guardrails, and token metering weighted by input type, with an on-prem option.
Multimodal assistants are where the AI assistant market is concentrating, driven by real workplace inputs like scans, screenshots, and spreadsheets. Treat market projections as directional; the verifiable point is that ingestion and citation quality across modalities, not chat fluency, separates products that teams rely on from toys.
Main features of our Gemini-style assistant
The headline layer above the per-panel detail.
Grounded citations
Knowledge-base answers carry per-claim source links, whether the input was a photo or a document.
Voice in and Read-aloud
Spoken questions and spoken answers for hands-busy and accessibility use cases.
Workspace knowledge bases
Team-scoped retrieval sources with guardrail policies applied per workspace.
Token metering by modality
Usage priced by input type and size, with tenant dashboards that update in real time.
Shared chats
Conversations shareable by link, with permission-aware history and search.
On-prem option
The full stack, metering included, installs on infrastructure the buyer controls.
Who should launch a multimodal AI assistant?
What these buyers share: users whose real work arrives as images and files, and the intent to run a grounded assistant over that reality.
AI-first founders
Target professions that live in documents and images, from engineering to medicine to logistics, with citations as the trust feature.
Vertical SaaS companies
Embed an assistant that understands your users' real artifacts, grounded in the data your platform already holds.
Enterprise and government suppliers
Deliver private deployments where external APIs are prohibited, with the on-prem option and retention controls procurement requires.
Edtech platforms
Build tutors that see what the student sees, with grounded answers instead of confident guesses.
Digital agencies
Ship a branded multimodal assistant for corporate clients, with workspace controls included since every panel ships in the build.
Feature panels of your Gemini-style assistant
Three products, one multimodal platform. Every panel ships in every build.
User assistant app
One assistant for text, images, files, and voice.
- ✓Multimodal chat with image upload
- ✓Document summarization and Q&A
- ✓RAG answers with citations
- ✓Voice input and read-aloud
- ✓Camera capture for visual questions
- ✓Prompt suggestions by task
- ✓Conversation history and search
- ✓Shared chats with links
- ✓Dark and light themes
- ✓Feedback capture per response
Workspace console
For teams embedding the assistant in daily work.
- ✓Team seats and shared knowledge bases
- ✓Workspace-scoped RAG sources
- ✓Guardrail policy configuration
- ✓Usage and token dashboards
- ✓Prompt and workflow templates
- ✓Audit logs
- ✓Data retention settings
- ✓API key management
Admin panel
Operate models, policy, and revenue centrally.
- ✓Model routing and fallback rules
- ✓Token metering and plan limits
- ✓Cost dashboards per model and tenant
- ✓Guardrail and blocklist editor
- ✓Content moderation queue
- ✓Plan and billing management
- ✓Abuse throttling
- ✓On-prem deployment tooling
The admin control plane: running the assistant as an operator
Four consoles, one view of models, knowledge, and money.
Model console
Model routing and fallback rules, cost dashboards per model and tenant, and provider connector management, so unit economics stay visible and tunable.
Knowledge and policy console
Guardrail and blocklist editor with versioning, the content moderation queue, ingestion health monitoring, and per-workspace RAG source administration.
Workspace console
Plan and billing management with modality-weighted metering, real-time tenant usage dashboards, and retention and audit configuration per workspace.
Ops console
Abuse throttling, on-prem deployment tooling for data-sensitive buyers, incident trace replay, and revenue reporting across subscriptions, seats, and API.
How a multimodal question gets answered
From image or document upload to grounded, metered response.
01
Input arrives
The user sends a prompt with an image, PDF, or spreadsheet; the request authenticates and the attachment is validated and stored.
02
Normalize the modality
Images get captioned and OCR'd, documents get chunked, and everything is turned into retrieval-friendly representations.
03
Retrieve grounding context
Relevant passages from workspace knowledge bases join the prompt so the answer can cite rather than guess.
04
Route and generate
Routing picks the model by task, generation streams the response, and visual or tabular questions use their specialized context.
05
Screen and cite
Output screening runs, per-claim citations link to the source material, and the answer renders with its evidence.
06
Meter by modality
Tokens and input types deduct against the plan with modality-specific weights, updating tenant dashboards in real time.
AI inside the platform
Five model-driven systems working the same request pipeline. Here the AI stack is not a feature layer, it is the product.
Multimodal ingestion
Normalizes images, PDFs, and spreadsheets into retrieval-friendly representations so a chart screenshot gets the same citation quality as a document.
Reads
Image content, OCR output, document structure
Changes
Real workplace inputs answered, not just typed prompts
RAG retrieval engine
Hybrid retrieval over workspace knowledge bases grounds every answer, with per-claim citations users can check.
Reads
Document embeddings, query intent, workspace scopes
Changes
Answers teams rely on because they can verify them
Cost-aware model router
Routes each task to the model that serves it at the right cost, from quick chat to heavy visual reasoning.
Reads
Task type, modality mix, token budgets
Changes
Multimodal margins that hold as usage scales
Guardrails pipeline
Screens inputs and outputs per workspace with versioned policies, blocklists, and full audit logging.
Reads
Prompt and output content, policy versions, tenant context
Changes
Enterprise deployments that pass security review
Voice pipeline
Handles spoken questions and read-aloud answers for hands-busy and accessibility scenarios.
Reads
Speech input, intent signals, playback context
Changes
Assistant access in kitchens, wards, and job sites, not just desks
platform concepts
Concepts that run a multimodal assistant
Multimodality is a normalization problem before it is a model problem. A chart screenshot, a scanned contract, and a spreadsheet all become structured representations on ingestion, which is why the same citation quality applies whether the user uploaded a photo or typed a question.
Workspace scoping and metering make it a product. Knowledge bases are tenant-scoped, guardrails apply per workspace, and billing weights differ by input type and size, so an enterprise deployment is auditable down to which team asked what and what it cost.
Grounding discipline separates a demo from daily use. Every knowledge-base answer carries per-claim citations, ungrounded questions are labeled as model knowledge, and on-prem deployment keeps the entire pipeline inside the buyer's wall for regulated data.
How your AI assistant platform earns
Subscription tiers metered by tokens, with multimodal inputs priced by type and size, all configured in the admin panel.
Workspace plans for teams: seats, shared knowledge bases, and admin controls sold per organization.
API access for developers embedding multimodal Q&A, billed with rate limits and overage.
Enterprise contracts: private deployment, custom guardrails, and fine-tuned behavior as annual packages.
Every rate and limit is admin-configurable, so pricing follows model costs instead of being frozen at launch.
Feature groups that decide whether teams rely on it
Four groups, each mapped to the part of the multimodal promise it protects.
Multimodal ingestion
Images, PDFs, spreadsheets, and voice enter one pipeline, normalized into retrieval-friendly representations with captioning and OCR, so a photographed chart answers like a typed question would.
Grounded answers
Retrieval over workspace knowledge bases with per-claim source citations, whether the input was a photo or a document, and traceable pipelines so a wrong answer is diagnosable rather than mysterious.
Workspace governance
Team-scoped RAG sources with guardrail policies applied per workspace, data retention settings, audit logs, and API key management, the control layer enterprises require before rollout.
Metering and economics
Tokens priced by input type and size with modality-specific weights, real-time tenant dashboards, cost dashboards per model and tenant, and on-prem deployment tooling, so unit economics stay visible as usage grows.
insight
The market numbers behind the multimodal assistant opportunity
Enterprise AI assistants keep compounding toward the hundred-billion-dollar mark globally by 2030 on widely published forecasts, with multimodal understanding the axis the market is concentrating on because workplace reality arrives as scans, screenshots, and spreadsheets rather than typed prompts.
The engineering wedge is ingestion and citation quality across modalities, not chat fluency: teams rely on assistants that ground answers in their own files and cite per claim. Operators who ship that trust layer win organization-wide deployments. 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 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 |
|---|---|---|---|---|
| Token subscriptions | Monthly tiers metered by tokens, priced by modality | Plans commonly $20 to $200+ per month | Answer quality. Tiers hold while answers stay grounded | |
| Workspace plans | Seats, shared knowledge bases, admin controls | Workspaces commonly $10 to $50+ per seat per month | Team stickiness. Seats grow while the assistant stays in daily work | |
| API access | Developer multimodal Q&A with rate limits | API spend commonly $0.10 to $15+ per 1M tokens passed through | Integration depth. API revenue scales while docs stay good | |
| Enterprise contracts | Private deployment, custom guardrails, fine-tuning | Contracts commonly $10,000 to $500,000+ per year | Trust and data control. Contracts hold while citations prove themselves | |
| Knowledge base fees | Usage-based storage and retrieval for RAG | Fees commonly $50 to $2,000+ per month per tenant | Corpus size. Fees scale while ingestion stays healthy | |
| White-label licensing | Branded deployments for resellers | 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 every panel, plus branded customer communications.
Store submission and launch
App store and Play Console submissions handled end to end, with production monitoring switched on.
Payment integrations
Stripe, PayPal, and regional gateways configured for subscription and token-metered billing.
AI models Pre-tuned
The multimodal ingestion, RAG pipeline, and guardrails arrive configured for assistants, not as raw APIs.
Multi-language and Multi-currency
Languages, currencies, and tax display configured for your markets during the launch window.
Scalable cloud infrastructure
Containerized services on AWS or Azure with autoscaling and queue-backed intake for peak traffic.
Admin analytics and reports
Operational dashboards, finance reconciliation views, and 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 version upgrades.
Technology inside the platform
Multimodal assistants are retrieval and routing problems wearing a chat interface.
Multimodal ingestion
Images, PDFs, and spreadsheets normalized into a retrieval-friendly representation, so a chart screenshot gets the same citation quality as a text document.
RAG and citations
Hybrid retrieval with grounded answers and per-claim source links. Retrieval tuning detail sits in our RAG development services.
Guardrails
Input and output screening with blocklists, escalation, and audit logs, configured per workspace.
Deployment
Cloud or on-prem with token metering and model routing. To embed custom capabilities, our AI integration services extend the platform.
Integrations
Model providers, storage and document services, SSO providers, and payment gateways ship as configured integrations, so the platform plugs into the systems your users already run on.
Security and compliance
Data retention controls, role-based access, append-only audit logs, encrypted traffic and secrets, and on-prem tooling come standard, so enterprise and regulated-buyer due diligence has answers before it is asked.
Launch timeline: first multimodal answer 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
Users, plan structure, and modal priorities locked with a written scope, and your identity applied across all three panels.
✓ step 01 complete - 02
Days 3-4: configuration and integration
Model providers, token weights, guardrail policies, and SSO configured and tested against your live accounts.
✓ step 02 complete - 03
Days 5-6: supply onboarding and pilot
Sample documents and image corpora ingested so RAG answers cite real sources, then a pilot: image question, document Q&A, metering, guardrail block.
✓ 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 and product updates included.
✓ step 04 complete
Almost-ready AI launch or custom build: the honest comparison
Both tracks ship the same multimodal 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 | Dispatch, forecasting, and personalization pre-tuned for the vertical | AI services designed around your workflows and data |
| Modal and data fit | Images, documents, and voice configured on the proven core | Custom ingestion and connectors built to your data estate |
| 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 it a hard question before you commit.
Book a walkthrough and we will demo multimodal input, citations, and the workspace console 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
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 ingestion pipelines, connectors, or on-prem hardening add scoped estimate work.
Deployment targets
Cloud regions, on-prem environments, and data-retention rules are configured per buyer. Adding deployment targets means more configuration, never a rebuild.
Integrations
Model providers, SSO systems, and storage services are wired during the build, and each integration is estimated individually.
AI tuning depth
Out of the box, routing, retrieval, and guardrails are pre-tuned for multimodal assistant workloads. Fine-tuning 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 over workspace data with deterministic step logs, extending Q&A into automated work.
Meeting and screen capture
Assistance over live meetings and screen content, the workplace input type that keeps the assistant inside daily work.
Mobile companion suite
Native voice-first and camera-first experiences, meeting users where questions actually arise.
Analytics and deflection reporting
Answer quality sampling, resolution and deflection reporting, and cost-per-answer dashboards, the evidence that renews enterprise contracts.
Compliance pack
Data residency configuration, retention policies, and export controls mapped to regulated buyers, sold as an annual add-on.
White-label reseller licensing
Deploy the platform for consultancies under their own identity, with per-tenant workspaces, knowledge bases, and model configuration.
/21
Why choose our Gemini-style build
Assistant platforms win on trust, grounding, and unit economics.
Grounded answers
Citations on every knowledge-base answer are the difference between a demo and a product people rely on.
Multimodal from day one
Images and documents are first-class inputs in the retrieval pipeline, not a bolted-on preview feature.
AI depth on record
120+ AI products in production with a 98% deployed accuracy benchmark across our systems.
Pair it with Text-first
Run a reasoning-focused sibling product on the same stack family; see our ChatGPT-style assistant track.
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 reliability bar every AI deployment is held to.
1M+
Monthly Users
40%
Engagement Lift
Marvel Studios
A high-traffic media platform our engineering system ships at franchise scale, the same quality bar every consumer AI product is held to.
10M+
Streams
65%
Completion Rate
PragPlay
A high-concurrency platform our engineering system ships and operates at 99.9% uptime, the same bar every AI platform is held to.
500K+
Players
99.9%
Uptime
Frequently asked questions
Security and integrity
Tap a card to see how the platform defends its answers.
Citations Attach Per Claim
Knowledge-base answers carry per-claim source links whether the input was a photo or a document, so users verify rather than take the model's word.
Every Modality Gets Normalized
Images get captioned and OCR'd, documents get chunked, and everything enters retrieval-friendly representations, so workplace reality answers like typed prompts do.
Guardrails Scope to Workspaces
Guardrail policies apply per workspace with versioned, auditable changes, so a law firm and a product team can share a platform without sharing a rulebook.
Metering Sees Input Types
Tokens price by input type and size with modality-specific weights, and tenant dashboards update in real time, so unit economics stay visible before they become surprises.
Wrong Answers Are Diagnosable
Traceable pipelines record what context the model saw at every stage, so an error becomes an engineering input instead of an anecdote.
On-Prem Stays on the Table
The full stack, metering included, installs on infrastructure the buyer controls, which is what regulated buyers require before signing.
Frequently asked questions
the process
What to expect during your engagement
You start with a discovery call covering users, plan structure, and modal priorities, then receive a written quote. Once you approve, you hand over your brand assets and we configure the platform: your identity across all three panels, your model mix, token weights, and guardrails. The almost-ready AI build goes live in 6 working days; a custom build takes 3 to 4 weeks, planned with you up front.
Post-launch, 60 days of included support and 12 months of updates cover the model and platform changes that arrive constantly in this category. The first 90 days are a grounding phase: loading real workspaces with documents and image corpora, watching citation accuracy across modalities, tuning token weights against actual usage mix, and reviewing guardrail logs with your first enterprise users. Multimodal assistants get adopted when the answers are checkable, and the platform is what makes them checkable.
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.
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$ moxace launch --nowShip a multimodal assistant your market can trust.
Book a free consultation and get a written scope and quote.
- 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.
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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.
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