Knowledge Spaces
AI Integration Services

AI Integration Services for Enterprise and Government

Connect AI to the systems and data you already run, governed, audited, and production-ready. We integrate AI through the Knowledge Spaces control layer, so every model call is scoped, permissioned, and traceable.

01 — The Problem

Most AI stalls at the integration layer

Pilots are easy. The hard part is connecting AI to your real systems, current data, permissions, and workflows, without leaking data, losing traceability, or locking yourself to one vendor. That is the work we do.

An assistant that cannot see your CRM, your document store, or your line-of-business database is a demo. Integration is what turns it into a system of record your team can rely on. We handle the connection, the governance, and the guardrails so the AI uses approved, current information and acts safely inside your environment.

02 — What We Integrate

Your systems, connected to governed AI

We connect AI to the operational systems and knowledge sources your organization already depends on.

CRMs & line-of-business apps

Salesforce, HubSpot, order and case management, and internal applications through their APIs.

Databases & data warehouses

Postgres and other databases, so assistants reason over live operational data, not stale exports.

Document & file stores

Policies, SOPs, contracts, and knowledge bases, ingested and kept current under governance.

Internal & partner APIs

REST services and pipelines. If the API you need does not exist, we design and build it.

Approved web & live feeds

Specific approved URLs and market or reference feeds for real-time context.

Any model, your choice

Route to the best LLM per use case on speed, accuracy, and cost, or bring your own keys.

03 — How We Work

Integration the governed way

Every integration runs through Knowledge Spaces, the control layer between your data and the models, so nothing is ad hoc.

01

Map & scope

Identify the systems, data, and workflows the AI needs, and the boundaries it must respect.

02

Connect & govern

Wire up connectors and APIs; set who and what each assistant can access, per bot, user, and space.

03

Deploy

Ship via embedded widget or API, wrapped in your own branded experience.

04

Stay accountable

Every retrieval and answer is logged and traceable for audit and continuous tuning.

Minimum-necessary retrieval, by design

We never hand a model your whole database or document set. Only the minimum necessary context is retrieved from approved sources for each request, isolated per tenant, and logged. Your data stays yours, and switching models later is a configuration change, not a rebuild.

04 — What We Build

From connectors to custom APIs

05 — In Practice

Integration use cases

Enterprise

Assistant grounded in live CRM data

Connect an order or customer system so the assistant references current status and logistics alongside approved documentation, with every access logged.

Government

Compliance assistant over policy & regulation

Ground answers in FAR, CAS, and internal policy through a governed space, with citations and traceability that support audit readiness.

Multi-tenant

One platform, many isolated clients

Give each client their own isolated tenant and private spaces, combining your core knowledge with their data without ever mixing the two.

Operations

Governed dashboards from connected data

Pipe operational data into Knowledge Spaces and surface governed, cited insight in dashboards tailored to your metrics.

06 — Why Sprinklenet

Governed, fast, and built to last

A control layer, not glue code

Integrations run on Knowledge Spaces, so governance, isolation, and audit are built in, not bolted on.

Six weeks to production

A typical integration reaches production in about six weeks, whether you drive it or we do.

Enterprise and government ready

Built for regulated environments, with a GSA Multiple Award Schedule for federal buyers.

07 — FAQ

AI integration questions

What is AI integration?
Connecting AI models to your real systems and data, CRMs, databases, document stores, and internal APIs, so the AI can use current, approved information and act inside your workflows, rather than answering from general knowledge alone. We do it through a governed control layer so every connection is scoped, permissioned, and audited.
How do you keep integrated AI governed and secure?
Every integration runs through Knowledge Spaces, the control layer between your data and the models. Retrieval is scoped per bot, user, space, and workflow; only the minimum necessary context reaches the model; and every access is logged for audit. Your data stays yours, and you choose the model and its retention terms.
What if the API we need does not exist yet?
We design and build the missing API so your critical systems can participate safely. Custom connector and API development is part of our integration work.
Are we locked into one AI vendor?
No. The platform is model-agnostic. You can bring your own model keys or route to the best model per use case, and switching later is a configuration change rather than a rebuild.
How long does an AI integration take?
A typical integration goes from pilot to production in about six weeks, delivered via embed or API, whether your team drives it or we do.
Get Started

Ready to integrate AI into your systems?

Tell us the systems and workflows you want AI to work inside. We will scope a governed integration and a path to production in about six weeks.