Contact us

Enterprise AI Orchestration

One AI platform to scale AI across your company

Build, deploy, and manage AI agents, automate workflows, and empower every employee to use AI—all from one secure platform.

Talk to Sales Explore Platform
Task
Run by
Model
Eval
Cost

Unified AI Architecture

The complete platform for
enterprise AI adoption

Leapforce combines agents, workflows, connections, governance, infrastructure, and enablement in one place. Give every team the power to automate business processes while maintaining the visibility and control your enterprise needs.

Team AI adoption

Active users by week

Accelerate adoption

Built-in training helps employees develop the skills to use AI effectively in their everyday work.

142 agentsOne platform +20.36%
38 tools replacedWorkflows · access · governance −62%

Consolidate your AI stack

Replace disconnected tools with one platform for agents, workflows, integrations, access, and governance.

1,240 ↑ 12%

Workflows / day

24k

Scale with confidence

Move from initial use cases to company-wide deployment on secure, enterprise-ready infrastructure.

System Interoperability

AI agents that work
across your systems

Connect AI agents to the tools, data, and applications your business already uses. Leapforce brings your systems together so agents can find information, perform tasks, and coordinate work across departments.

Centralized connections

Manage company applications, data sources, and credentials from one place.

Permission-aware access

Control which users, teams, agents, and workflows can use each connection.

Reusable integrations

Configure connections once and securely reuse them across approved agents and workflows.

Connection monitoring

Maintain visibility into how agents interact with your business systems.

Cross-Department Routing

Marketing Campaign Agent Verified Protocol
HR Onboarding Assistant Verified Protocol
Finance AP Invoice Evaluator Verified Protocol
IT Helpdesk Automator Verified Protocol

Department Enablement

Bring AI into every business function

Leapforce helps teams apply AI directly to their existing processes, without forcing employees to adopt another collection of disconnected tools. Deploy agents across sales, marketing, operations, finance, customer service, human resources, and other departments. An expert can create an agent or workflow once. The rest of the organization can use it through a controlled, repeatable experience.

Enterprise Security

Give teams the power to build.
Make AI knowledge reusable across the company.

Leapforce provides the authentication, permissions, governance, and visibility required to manage AI across an enterprise. Set your controls centrally and apply them consistently across every user, agent, workflow, and connection.

Enterprise authentication

Connect Leapforce to your organization's identity and access environment. Centralized authentication, role-based access controls, team and department permissions, user provisioning and deprovisioning, administrative access management.

Agent access controls

Define who can create, edit, approve, publish, and use each agent.

Connection permissions

Control which systems and data sources are available to each user, team, agent, or workflow.

Activity records

Maintain visibility into important actions across the platform.

Central Registry

Manage your entire AI workforce
from one place

Leapforce gives your organization a centralized environment for creating, organizing, governing, and improving AI agents.

Central agent registry Keep every approved agent accessible from a searchable company-wide directory.
Ownership and permissions Assign owners and determine which employees or teams can access each agent.
Agent lifecycle management Manage agents from development and testing through approval, deployment, and retirement.
Version management Improve agents while maintaining control over published versions and changes.
Performance visibility Monitor how agents are used and identify opportunities to improve their results.

Agent Registry

···
Marketing Campaign Agentv2.4 · Marketing Published
Finance AP Invoice Evaluatorv3.1 · Finance In review
HR Onboarding Assistantv1.8 · Human Resources Published
IT Helpdesk Automatorv4.0 · IT Operations Deployed

Workflow Orchestration

Turn business processes
into workflows

Build repeatable workflows that coordinate AI agents, employees, and connected systems. Leapforce helps teams automate complex processes while keeping important decisions visible and under human control.

Workflow Engine

LIVE
1,240 runsAgents
32 approvalsHumans
01 Multi-step automation Combine agents, actions, data, and business rules into structured workflows.
02 Human approvals Add review and authorization stages wherever human judgment is required.
03 Reusable workflow templates Standardize effective processes and deploy them across teams.
04 Workflow governance Control who can build, edit, publish, and execute workflows.
05 Execution monitoring Track workflow runs, errors, outcomes, and operational bottlenecks.
06 Cross-organizational reuse Share proven workflows across departments to scale best practices faster.

Unified AI Governance

Govern every AI connection without adding IT overhead

Give every employee and agent secure access to the data and tools required for their job. Leapforce automatically applies policies based on department, role, agent, task, and data sensitivity from one central MCP gateway.

Explore AI governance

Activity

All activity across all MCP servers

Last 24 hours

ServerTimeToolUserLatencyStatus
Rows per page Page 1

One MCP gateway for every connection

Connect agents, enterprise systems, and MCP servers once, then manage them centrally.

Hybrid execution, automatically

Hybrid Agents automatically choose on-device, on-premise, or cloud execution based on data sensitivity, cost, performance, and model requirements.

Access matched to the job

Automatically grant least-privilege access based on who is requesting data, which agent is acting, and the task being performed.

Reduce IT manual work

Replace one-off permissions, credential management, and access reviews with reusable policies applied across every agent and workflow.

Deployment Flexibility

Deploy AI your way

Run Leapforce in the environment that meets your organization's security, compliance, and operational requirements.

Run on Cloud

Deploy quickly on our fully managed cloud platform. We handle infrastructure, scaling, maintenance, and updates.

Secure tenant isolation

Keep every organization's data, agents, credentials, and workloads separated through strict tenant-level security boundaries.

Self-hosted deployment

Run Leapforce in your own cloud, VPC, or on-premise infrastructure for complete control over data and operations.

AI Cost Optimization

Get more value from
every AI dollar

Automatically route every request to the right model, reduce token usage, and track spend across teams, workflows, and agents.

Routing quality

Requests / week

36% ↑

$0.02 / req

Intelligent model routing

Automatically route every request to the best-performing and most cost-efficient LLM based on quality, latency, and price.

Raw tokens 48,200 85%
Compressed 6,900 18%
Accuracy kept 99.4% 99%

Context compression

Compress prompts and conversation history before every inference to reduce token usage by up to 90% without sacrificing accuracy.

Requests routed36% ↑

13,690

3,120 reqs

Multi-LLM orchestration

Connect OpenAI, Anthropic, Google, Azure OpenAI, local models, and more through a single enterprise gateway.

Spend by team

This month

18% ↑

$4,120

Department cost allocation

Track AI spending by department, team, project, workflow, or agent to understand ROI and control budgets.

Active workflows 212 70%
Flagged for review 14 22%
Savings identified $9,340 58%

Usage analytics & optimization

Monitor token consumption, identify expensive workflows, and uncover opportunities to reduce AI costs automatically.

Budget cap forecast

$12,480−14% vs limit

Budget cap forecasting

Set hard spend ceilings per team or workflow and get an early forecast the moment usage trends toward the limit.

Hands-On Enablement

Not another platform. We help you put AI to work.

Leapforce works alongside your teams to capture knowledge, redesign workflows, build and manage agents, and deliver role-specific training directly within the tasks where people need it.

SOPs captured 86 78%
Workflows drafted 34 52%
Approved for reuse 27 68%

Capture how work gets done

Turn your team's knowledge, processes, and best practices into repeatable AI workflows.

Agent builds

By team this quarter

42% ↑

12 agents

Help your team build agents for real jobs

Guide teams from identifying use cases to building agents that perform specific tasks and responsibilities.

Agents under management24% ↑

312

Healthy

Manage and improve every agent

Monitor performance, refine workflows, and keep agents aligned as your business evolves.

Training completion

91%+22% this quarter

Train people in the flow of work

Deliver practical guidance at the moment of need, tailored to each employee, role, and task.

Strategic Partners

Professional support from strategy to scale

Our team works alongside yours to move high-value AI opportunities into production faster.

Opportunities scored

Discovery workshops

31% ↑

Top pick

AI opportunity discovery

Identify processes where agents and workflows can create the greatest business impact.

Systems connected 18 76%
Workflows deployed 42 64%
Governance reviews 9 40%

Implementation support

Design and deploy solutions around your systems, processes, and governance requirements.

Onboarding progress18% ↑

94%

Live

Enterprise onboarding

Configure authentication, connections, permissions, infrastructure, and cost controls.

Adoption momentum

Active seats

27% ↑

870 seats

Adoption support

Help teams build confidence and integrate AI into their everyday work.

Cost efficiency gained

+19%since last quarter

Ongoing optimization

Improve agents and workflows as usage grows and business needs change.

Ready to build your AI Workforce?

Stop paying for the work. Start earning from it.

Book a Demo Explore Platform

Comprehensive answers to the most commonly asked questions

What is an enterprise AI agent platform?
An enterprise AI agent platform is a centralized environment for building, hosting, governing, sharing, and managing AI agents across an organization. It connects agents to approved company data through governed integrations, applies enterprise permissions to every action they take, and keeps the agents themselves under company ownership. The distinction that matters is asset ownership: a personal AI tool produces output for one person, while an enterprise agent platform turns that same work into infrastructure the whole company can inspect, reuse, and keep.
Why should enterprises host AI agents centrally?
Central hosting gives you one place to manage access, cost, and compliance. Instead of scattered personal subscriptions and unmetered API keys, every agent runs under shared policies, with usage and spend visible to the teams that own them. It also removes duplicated work: when Sales proves out a workflow, Support can adopt it the same week rather than rebuilding it. Decentralized adoption feels faster for the first few weeks and gets expensive to unwind for years afterwards.
How is this different from employees using personal AI tools?
Personal tools leave data, cost, and knowledge outside the company. Prompts live in someone's account, spend lands on a personal card, and no one can audit what the agent touched. LeapForce keeps agents, their data access, and their output under central control — so value compounds for the organization instead of walking out the door. The work is the same; who owns it afterwards is not.
What happens to an agent when its employee owner leaves?
Agents are company assets, not personal ones. When someone leaves, their agents stay — ownership transfers to the team so the work, context, and integrations continue uninterrupted. Prompt templates, system instructions, data connections and historical run logs stay attached to the agent rather than the account, and builder access is deprovisioned centrally. The institutional knowledge encoded in a workflow is usually worth more than the workflow itself.
Can teams share and reuse AI agents?
Yes. Prove an agent out once, then publish it so other teams can reuse it — with the same guardrails — instead of rebuilding the same automation over and over. Shared workspaces, agent cloning, collaborative editing and pre-packaged templates mean a proven workflow spreads across departments as a governed asset, not as a copied prompt pasted into a chat window.
Can AI agents connect to proprietary enterprise data?
Yes. Agents connect to your approved systems and data sources through governed connectors, so they work with real company context while access stays scoped and auditable. Retrieval is permission-aware — an agent sees only what the person or team invoking it is entitled to see — and sensitive-data filters sit in front of the sources that need them. The integration is approved once at the platform, then inherited by every agent built afterwards.
How does LeapForce support AI agent governance?
AI agent governance on LeapForce is enforced at the platform rather than agreed in a policy document. Role-based access, audit logs, approval workflows and spend policies apply to every agent and every action, so you can see who did what, control permissions centrally, and keep spend in check as adoption grows. Each run leaves a full trace: which agent, which model, which data it touched, what it decided and what it returned.
Is the platform only for technical teams?
No. Anyone can build in plain language and LeapForce translates intent into working automations. Technical teams get deeper controls, but you don't need to write code to get started. In practice the best agents come from the domain expert who owns the process — the person who knows the exceptions — working inside safety rails an engineer configured once.
How long does deployment take, and does every tool need its own IT review?
The security review happens once, at the platform. After that, every department builds on connectors that have already been cleared, so deployment stops being a queue. This is the difference between approving a platform and approving a tool: the first is a single review that compounds, the second repeats for every new request. Teams that adopt agent platforms one tool at a time usually spend more time in review cycles than in building.
What security controls should an enterprise AI agent platform provide?
At minimum: granular RBAC, team-level permissions, automatic access reviews, and centralized deprovisioning when a builder leaves. Beyond identity, enterprise-grade security means agents run in your environment under your credentials, with scoped system access, permission-aware retrieval and sensitive-data filtering in front of the sources that need it. Every action is logged to an immutable audit trail, and any agent can be stopped instantly with a kill switch. Security that only exists at the login screen is not security for agents that take actions.
How do you keep AI spend under control as adoption grows?
Usage, subscriptions and model selection are centralized, and every task is routed to the right model on cost, speed, privacy and quality rather than defaulting to the most expensive one. Teams keep the AI services they already know while the company manages access and usage centrally, which removes duplicate subscriptions and unmanaged keys. Per-agent budgets, quotas and action guards cap exposure, and each agent reports what it cost against what it returned — so an agent that cannot justify itself gets retired instead of quietly renewed.
What is graduated autonomy?
Graduated autonomy is a deliberate path from assisted work to self-driving systems, in four stages. Assisted: a human triggers the work and the agent suggests a draft. Copilot: the agent drafts automatically and you approve the output. Autopilot: the agent executes and you audit the logs, with exceptions surfaced for you. Self-Driving: the agent governs the workflow, raises flags on exceptions and improves from feedback. The point is that trust is earned per workflow with evidence, instead of being granted to a whole category of work on day one.
Can several agents work together on one workflow?
Yes — this is where multi-agent systems earn their keep. A single request can hand off across specialists: one agent runs the query, another builds the visualization, a third publishes the digest to Slack, all from one prompt. Orchestrating agents this way keeps each one narrow and testable rather than building a single agent that does everything badly. Every hop in the chain is logged, so a multi-step run stays as auditable as a single-step one.
What does an AI agent management platform handle that a chat tool doesn't?
Lifecycle. An AI agent management platform gives you a searchable catalog with ownership and status tracking, usage and dependency metrics, full immutable version history, approval workflows before a change ships, frictionless ownership transfer, and archiving when an agent is retired. A chat tool gives you a conversation. The difference shows up the first time someone asks which version of an agent produced a given result six months ago.
How does the platform scale across departments?
Agents run on scalable cloud infrastructure with Kubernetes orchestration, auto-scaling and redundancy, so a workflow that worked for one team does not fall over when five more adopt it. Scale is as much organizational as technical: a governed library of approved agents and connectors means each new department starts from proven work rather than from zero. Sales, Support, Operations, Finance and HR reuse the same building blocks under the same policies.
How is this different from building agents on a developer framework?
Agent development frameworks give engineers primitives and leave governance, cost control and lifecycle as an exercise for the reader. An enterprise agent platform ships those as the product. With a no-code agent builder the domain expert who owns a process can build it directly, with full safety rails, instead of translating requirements to an engineer and back. Frameworks are excellent for bespoke systems; they are a slow way to get a finance team a working invoice agent.
Can we keep the tools and models we already use?
Yes. Integration is the point rather than replacement — agents connect to more than a thousand tools teams already work in, and an AI gateway sits in front of model providers so you are not locked to a single vendor. Teams keep their existing AI subscriptions while the company gains central visibility over access and usage. A seamless integration layer means adopting the platform does not start with a migration project.
What does enterprise AI automation actually replace?
Repetitive, judgment-light work that currently consumes people who were hired for judgment: keying invoices, triaging tickets, chasing renewals, assembling the same report every Monday. Enterprise AI automation is not about removing the human from the workflow — it is about moving them to the exceptions. That is also what separates it from the last generation: classic enterprise automation ran on rigid rules and broke on the first case nobody scripted, whereas an agent handles the exception path instead of failing at it. The measure of whether it worked is not how many tasks ran, but whether the team got its attention back for work only people can do.
Can we get agents built for our specific industry?
The catalog covers 104 specialist agents across 13 enterprise verticals — healthcare, finance, supply chain, manufacturing, legal and compliance, HR, marketing, support and more — each built around a real workflow with its systems, its exceptions and the outcome it has to hit. For custom enterprise requirements our team configures bespoke orchestrations, private model layers and industry-specific compliance guardrails on top of the same governed foundation.
How do you prove ROI from agentic AI?
Every agent reports what it cost and what it returned, measured against budgets you set, so the conversation moves from anecdote to evidence. That means unit economics per agent, spend attributed to the team or prompt that generated it, cost-anomaly detection, and visibility into shadow AI running outside the platform. Most agentic AI programs stall because nobody can defend the invoice; the fix is to make cost and return a property of the agent rather than a quarterly reconstruction.
What happens when an agent gets something wrong?
You find out, and you can undo it. Before you deploy AI agents against real systems, action guards define what each one is allowed to touch; exceptions are surfaced rather than swallowed; and every run leaves a full trace of the model used, the data read and the decision taken. Any agent can be stopped instantly with a kill switch, and immutable version history means you can see exactly which version produced a given output and roll back to the one before it. Agents will get things wrong — the question a platform has to answer is whether you can prove what happened and reverse it.
How do we find the unmanaged AI already running in the company?
Shadow AI is usually discovered through the invoice rather than the org chart: personal subscriptions, unmetered API keys and side accounts nobody owns. LeapForce surfaces AI spend and usage centrally, including activity happening outside the platform, so you can see what is actually running before deciding what to sanction, migrate or shut down. The practical move is rarely a ban — it is giving those teams a governed path that is easier than the workaround they built.
Who should own the AI agent programme internally?
Split it. A central team owns the platform — identity, approved connectors, budgets, policy — and each department owns the agents it builds on top. Most AI agent platforms fail in one of two directions: either IT owns everything and becomes the bottleneck for work it does not understand, or every team runs unsupervised and the company inherits an ungoverned estate. Enterprise AI automation works when the domain expert who owns a process builds the agent, inside rails someone else configured once.
Where does our data live, and who can see it?
Agents run in your environment, under your credentials, on your cloud — your VPC and your keys, with a full audit trail of every access. Retrieval is permission-aware, so an agent returns only what the requesting person or team is already entitled to see, and sensitive-data filters sit in front of the sources that need them. Commercial agreements, DPAs and PII handling are part of the contract rather than an afterthought. If a vendor cannot tell you which cloud your data sits in and who holds the keys, that is the answer.
How do you get employees to actually use the agents?
Enablement is the part most rollouts skip, and it is why adoption stalls at the handful of people who worked it out alone. Training lands where the work already happens — Slack, Salesforce, Zendesk — at the moment someone needs it, rather than in a portal nobody opens or a session scheduled for next quarter. Because agents are shared assets, one team's proven workflow becomes the starting point for the next, so each department adopts from something that already works instead of from a blank canvas.