Strategy-firm rigour, engineering depth, and a Silicon Valley view of what is possible.

The last generation of SaaS left enterprises with platforms that do not talk to each other and data sitting in systems that were never connected, so the insight in that data rarely reaches a decision.

An operating system of the business is one model of how your business works, built on and connected to the systems you already run:

• the objects your teams talk about: customers, products, orders, assets, suppliers, contracts
• the actions the business can take, and the rules that bound them
• the models that decide, held to a quality floor

Every AI system we build sits on that one model, so it inherits what the last one built instead of starting from a prompt and a raw export. For an enterprise it is the difference between a series of disconnected pilots and a capability that compounds.

• A consultancy leaves a strategy and a roadmap.
• A software vendor leaves a licence for a product you configure.
• An applied-AI firm builds and runs working systems inside your operation, on your data, measured by the return they produce.

Collective Intelligence Group is the third kind: strategy-firm rigour, engineering depth, and a team that stays to run what it builds.

Because a demo that works on a good day is not a system an enterprise can run on. Vibe-coding gets a prototype in front of people fast, and we encourage it: it shows what is possible. What it does not give you is the engineering that turns a prototype into something a quoting desk or a finance team can rely on every day.

• A harness around the model. Evaluations that measure accuracy against your own history and hold it to a quality floor every week, guardrails that catch the wrong answer before it acts, and scenarios that show what a decision would do before it is taken.
• Reliability. Integrations with your ERP and CRM that survive month-end, data that reconciles, retries and monitoring when a system or a model provider fails, and a version of every rule and prompt you can roll back.
• Enterprise controls. An approval gate, permissions, an audit trail with sources, and deployment in your own tenant so security and legal can sign off.
• A foundation that compounds. One model of the business underneath, so the second use case builds on the first instead of starting again.

Prototypes prove an idea. Enterprise systems have to be right, on time, at scale, every day, and that is an engineering discipline, not a prompt.

Rent for experiments, build for the operation.

Subscriptions and platforms are fast to start, but the return on AI depends on three things:

• how much of your operating knowledge a system can act on
• how many use cases share that model
• whether the system is allowed to act

Those three compound only when you own the model of your business. We build that model with you, in your own tenant, and it stays yours.

• Scan. A short, fixed-scope engagement of one to two days on site.
• Pilot and production. Priced on the value they free up, against a baseline agreed before anything is built.

Proposals carry named assumptions and a change clause, so a surprise changes the price, not the relationship. There are no per-seat licences, and you own everything we build.

Start from the P&L line you already watch, not from the technology.

In a scan we map how the work actually runs in sales, service, operations, procurement and finance, size the payback of each candidate in your own numbers, and rank them. The first system is usually the one with the largest measurable return and the fewest dependencies, so it can run in a team's daily work within a quarter.

No. Enterprise operations still run on ERPs, shared mailboxes, PDFs, spreadsheets and people who know the rules.

Our systems read that data as it is, reconcile it into one model of the business, and carry lineage back to every source record. Data quality improves as a by-product of running the system, not as a project you have to finish first.

Yes. We connect to the systems you already run through their APIs, files and databases, including SAP, Microsoft Dynamics, NetSuite, Salesforce, HubSpot, PLM systems and in-house tools.

Nothing is replaced or migrated. Data reads up into one model of the business, and approved actions write back through a gate you control.

A person approves anything that matters.

• Systems draft, match, check and recommend on their own.
• Actions that write back to your ERP, go to a customer or commit to a supplier pass through an approval gate you configured and can see.
• Every action carries an audit trail with its sources, and the gate can be tightened or opened as trust is earned.

Because they are designed to impress rather than to run. A pilot built from a prompt and a data export cannot survive live data, exceptions and the tools a team already uses.

Pilots that scale:

• start from a P&L line with a baseline
• run in shadow mode on real data
• are allowed to act through a gate
• sit on a model of the business that the next use case can reuse

• Every model is held to a quality floor that is checked weekly against your own history.
• Systems run in your tenant, in your region, with role-based access, encryption and an audit trail that records every read, every action and the source behind every fact.
• Nothing you share trains shared models.
• A person approves any action with regulatory or contractual consequences.

Enterprises with real operations: industrial and consumer goods distributors, manufacturers, logistics and supply chain networks, and consumer goods companies, along with the private equity funds that own them. We deploy in the United States, Europe and Asia.

We are based in Silicon Valley, with teams in Singapore and Shenzhen, and we deploy on site with clients in North America, Europe and Asia. We work with research labs at leading universities so the most advanced models and agent tooling reach the operating problems of enterprise clients.