Enterprise connectors and MCP integrations
Scoped, audited connections from AI models to ERP, CRM, ticketing, and document systems, including MCP servers where they fit. Access is least-privilege and reviewable by security.
Agentic platforms & integrations
The connectors and controls your agents need
The connectors and controls your agents need
to work with real systems
We build the shared foundation agents run on: governed access to enterprise systems, cited answers from company knowledge, reviewed workflows, and monitoring that shows cost and accuracy.
Book discovery call
Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
Platform pieces
we build and run
Enterprise knowledge assistant
A conversational assistant that answers from your documents, wikis, and records, cites every source, and respects existing permissions so people only see what they are allowed to.
Workflow agentification
Manual, multi-step workflows rebuilt as production agents with evaluation sets, approval steps, and clear limits on what the agent may do without a person.
Agent access gateway
One controlled entry point for agents to reach company data and tools, with per-agent scopes, logging, and the ability to revoke access instantly.
Managed AI operations
Ongoing monitoring, evaluation reruns, model updates, and incident response for agents in production, run by tkxel or alongside your team.
AI spend controls
Per-workflow cost tracking, budget alerts, and automatic limits that stop runaway usage before it reaches the invoice.
Running several agents with no shared controls?
We can review what you have and show where a shared platform layer helps.
Book discovery callWhat happens when every team
builds its own agent?
Every agent has its own access keys
Credentials are spread across projects and nobody can list what each agent can reach.
Answers without sources
Assistants respond confidently, but staff cannot check where the answer came from.
Costs appear after the fact
Usage spikes are discovered on the monthly invoice, not when they start.
Nobody watches agents after launch
Accuracy drifts when data or models change, and no one is assigned to notice.
How we build
a shared agent platform
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We list the agents, systems, and data involved today and design the access, logging, and evaluation layers.
Deliverables: System inventory, platform architecture, access model
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Connectors and the access gateway are built for the highest-value systems first, with security review in each sprint.
Deliverables: Connectors, gateway, audit logging
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One or two existing or new agents run on the platform with evaluation, review queues, and cost tracking.
Deliverables: Production workflows, dashboards, cost alerts
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We run the platform with your team, add connectors as new use cases arrive, and rerun evaluations on every change.
Deliverables: Operations runbook, monthly reports, connector backlog
Featured success stories
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Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Give your agents governed access to real systems
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Frequently asked questions
What is MCP and do we need it?
The Model Context Protocol is an open standard for connecting AI models to tools and data sources. It helps when several models or agents need the same connections. We use it where it reduces duplication and use direct integrations where they are simpler.
Can the knowledge assistant respect our existing permissions?
Yes. The assistant checks each user’s access in the source systems before retrieving content, so people only receive answers drawn from documents they can already open.
Which systems can you connect?
Common targets include SAP, Oracle, Microsoft Dynamics, Salesforce, HubSpot, ServiceNow, Jira, SharePoint, Google Workspace, and custom databases or APIs. Older systems without APIs can be connected through integration layers we build.
Do you run the platform for us?
We can. Managed AI operations covers monitoring, evaluation reruns, model updates, and incident response. Many clients start with tkxel running it and move to shared or full internal ownership over time.
Will a shared platform slow down individual teams?
It can if it becomes a gate for every change, which is why we keep it thin: access, logging, evaluation, and cost controls. Teams still build their own agents on top. Where a team needs something the platform cannot yet support, we say so rather than block them.
How do you control AI costs?
Every workflow is tagged and tracked. Budgets trigger alerts at set thresholds and hard limits can pause a workflow automatically. Monthly reports show cost alongside accuracy so you can decide what to scale.