01 · Build
Describe it, then direct it
Start from a sentence or a blank project. Click any component on the canvas, say what should change, and watch it refresh. Restyle the whole app from a themes panel instead of opening a design ticket.
Dreamspace Enterprise
AI application development,
entirely within your infrastructure.
Dreamspace is a privately deployed application studio for enterprise-secure vibe coding, deployed by MakeInfinite Labs. Describe the app you need, shape it on a live canvas, and publish it to a real URL — against your data, behind your SSO, under your audit log.
Single-tenant installs on AWS, Azure, GCP or on-premises. No shared control plane.
acme-internal · canvas
What should your team build?
An intake dashboard for regional ops — pull open tickets from the warehouse, group them by region and age, and let leads reassign an owner without filing a request.
select a component, edit it
SQL + charts
internal APIs
themes
deployment
- Tenancy
- single-tenant
- Runtime
- your VPC · eu-central-1
- Identity
- Okta SAML → project RBAC
- Data
- stays in your account
- Models
- your keys, your region
- Audit
- every prompt, diff & deploy
Product notice
The public Dreamspace app is winding down. Community projects on dream.space stay reachable through the transition. Dreamspace continues as a privately deployed product for enterprises, delivered by MakeInfinite Labs.
The studio
The people closest to the problem build the tool.
The internal software backlog is mostly small, urgent, unglamorous work: a dashboard, a form, a lookup, a report. Dreamspace hands that work to the team that needs it, without handing your data to anyone.
02 · Connect
Wired to data you already have
Query your warehouse in SQL and drop the results in as tables and charts. Call your internal services and approved third-party APIs through the same gateway and credentials the rest of your stack uses.
03 · Ship
Publish like a real app
Name a version, save it, and publish to an internal subdomain or your own domain. Roll back to any earlier version. Hand a project to your platform team when it outgrows the studio.
04 · Govern
Reviewable from the first prompt
Every prompt, generated diff and deploy is logged against a named user. Security reviews the trail, platform sets the limits, and nothing reaches production without passing your existing checks.
Deployment
Installed where your data already lives.
MakeInfinite Labs deploys Dreamspace into your environment and stays on it — forward-deployed engineers, not a support queue. Three shapes, picked from your constraints rather than ours.
Your cloud
AWS · Azure · GCP — your account, your region
Dreamspace runs inside your VPC on infrastructure you own and can audit. Prompts, generated source, query results and published apps never cross the account boundary. The usual first choice when data residency is written into a contract.
Private SaaS
single-tenant — dedicated infra, managed end to end
Dedicated infrastructure, storage and model keys, isolated from every other customer and operated for you by MakeInfinite Labs. Fastest route to a pilot when you want the governance guarantees without staffing the install.
On-premises
restricted network · air-gapped · self-hosted models
For networks with no path to the public internet. Dreamspace runs against model endpoints you host, with updates delivered as signed, reviewable artifacts on your schedule.
Inside a private cloud tenant.
The most common shape is a single-tenant install in a cloud account your organization already owns and audits. Dreamspace becomes another workload in your environment — subject to your network policy, your key management and your change process.
Reference architecture
- Form factor
- Containers on your Kubernetes cluster, installed from a versioned chart
- State
- A managed Postgres database and an object storage bucket in your account
- Secrets
- Your secret manager and your KMS keys; nothing is held by MakeInfinite Labs
- Identity
- Your IdP over SAML or OIDC, mapped to Dreamspace roles
- Model access
- A private endpoint to your provider account, or a model you host in-tenant
- Egress
- Closed by default; the model endpoint is the only allowed destination
- Updates
- Signed release artifacts you pull and apply on your own change schedule
What this gets you
- A familiar review. Dreamspace is assessed like any other internal workload, not as a third-party SaaS processor.
- Residency that holds. Pin the tenant and the model endpoint to a region and the data never lawfully leaves it.
- Your own controls apply. Existing network policy, key rotation, backup and DR cover Dreamspace with no exceptions carved out.
- No vendor lock on the exit. Projects, generated source and audit records sit in storage you already own.
- Cost on your bill. Compute and model spend land in the cloud account and commitments you already negotiated.
Security & governance
Answers for the review before you ask for the pilot.
Identity
SAML and OIDC single sign-on against your IdP, with SCIM provisioning so leavers lose access the same day they lose their badge.
Access
Role-based access at the workspace and project level. Separate who may build, who may connect a data source, and who may publish.
Data
Your data stays in your account. Nothing is used to train models, and query results are never copied to a vendor-controlled store.
Models
Bring your own model keys or endpoints and pin them to a region. Route sensitive workspaces to a self-hosted model and leave the rest on a commercial one.
Audit
An append-only record of every prompt, generated diff, data connection and deploy, attributable to a named user and exportable to your SIEM.
Network
Private endpoints, IP allowlisting and no public egress by default. Published apps can be reachable only from inside your network.
Who deploys it
MakeInfinite Labs
A frontier research and development lab building AI and data solutions for the world’s biggest businesses. MakeInfinite Labs built Dreamspace, and its forward-deployed engineers are the team that installs, integrates and operates it inside your organization.
Forward-deployed engineering for enterprise AI apps and data infrastructure.How MakeInfinite Labs works
Engineers in your environment, not a ticket queue.
Forward-deployed engineers embed with your platform, data and security teams and work inside your infrastructure. They own the outcome end to end — from the first scoping session to apps your teams run in production. A Dreamspace engagement typically runs like this:
-
Scope
Pick the business unit, the data it depends on, and the first internal apps worth building. Agree on what a successful pilot measures.
strategy · architecture
-
Prepare the data
Build the pipelines, retrieval and query layers that make the relevant data accessible, trustworthy and AI-ready.
data engineering
-
Deploy
Install Dreamspace in your cloud or data center. Wire in SSO, access roles, data connections and model routing.
platform · security
-
Build alongside
Ship the first apps with your team, then train the people who will keep building after the engineers step back.
enablement
-
Operate & evolve
Monitor performance, improve reliability, and extend Dreamspace to more teams as the business grows.
ongoing
What a forward-deployed engineer actually does.
Forward-deployed engineers are not account managers and not a support tier. They are the lab’s own product engineers, working temporarily as part of your team.
Sits with your teams
Works alongside your platform, data and security engineers and the business unit that will use the apps — in your standups, your tickets, your review process.
Works in your environment
Deploys into your cloud account under your access controls, so the install is reviewed and operated the way the rest of your estate is.
Owns the outcome
Measured on apps your teams actually use, not on tickets closed. If Dreamspace needs a change to fit your stack, the engineer who deploys it can make that change in the product.
Hands it over
Trains your builders and platform owners as the work happens, so the engagement ends with your people running it rather than a dependency on the lab.
Engagement shape
Deployment sprint
A focused install: Dreamspace running in your tenant, wired to SSO and a first data source, with one business unit onboarded and a published app to show for it.
Engagement shape
Embedded pod
A small team of forward-deployed engineers working inside your organization for the length of a programme — data infrastructure, AI systems and Dreamspace rollout together.
Engagement shape
Operate & advise
Your teams own day-to-day building; the lab monitors reliability, ships upgrades, reviews architecture and steps back in when something new needs standing up.
AI consulting beyond Dreamspace.
Dreamspace is one part of a wider practice. MakeInfinite Labs partners with data-intensive businesses to prepare a modern data stack for enterprise AI, and provides end-to-end support to design, build and deploy AI systems that integrate with existing infrastructure, protect sensitive data and create measurable business value.
AI strategy & intelligent systems
Identify where AI will create measurable impact, define the right architecture, and build production-ready agents, copilots and workflows that fit naturally into the business.
Enterprise data infrastructure
AI is only as reliable as the data behind it. Secure data pipelines, retrieval systems and query infrastructure that make enterprise data accessible, trustworthy and AI-ready.
Production deployment & optimization
Shipping is the beginning, not the end. Deploy into your environment, monitor performance, improve reliability, and evolve AI systems as the business grows.
Areas of expertise
- Enterprise AI architecture
- AI agents & copilots
- Data engineering
- Distributed systems
- Cloud infrastructure
- Production platform engineering
- High-performance computing
- Cryptography & zero-knowledge proofs
Trusted AI starts with verifiable data.
Most AI systems assume the data they’re given is correct. MakeInfinite Labs builds systems that can prove it — combining high-performance cryptography with modern data engineering for organizations where accuracy, compliance and auditability are business-critical.
Space and Time ↗
Decentralized data infrastructure powering cryptographically verifiable database queries for AI systems and enterprise analytics.
Proof of SQL ↗
The industry’s leading open-source proving system for SQL, delivering sub-second proofs over large datasets in production.
Blitzar ↗
GPU acceleration framework behind Proof of SQL, integrated into Microsoft’s Nova proving system.
Dory implementation ↗
High-performance Dory polynomial commitment scheme, adopted by a16z crypto’s Jolt project.
Partners & customers
- Microsoft
- NVIDIA
- OpenAI
- Anthropic
- AWS
- Google Cloud
- Snowflake
- U.S. Bank
- FTI Consulting
- SS&C
Backed by
- M12
- Framework Ventures
- Samsung
- Fprime Capital
- Circle
Talk to a forward-deployed engineer.
Whether you want Dreamspace running inside your network or help with a broader AI initiative, contact us to discuss what you need.