AI Ops, Governance & Gateway

Operationalize AI with the governance and control your business requires

We help businesses govern AI usage, operationalize LLM workflows, and implement the gateway controls needed to scale AI without losing visibility, trust, or cost discipline.

Our clients

AI usage is spreading without clear governance

Teams are adopting AI tools, copilots, and agents before clear policies, ownership, approval workflows, and risk controls are in place.

AI outputs are hard to monitor and trust

Once AI workflows go live, teams need visibility into prompts, responses, failures, costs, latency, hallucination risk, and agent actions.

AI cost, access, and vendor usage are difficult to control

Different teams connect to different models, tools, and providers, creating fragmented access, rising LLM costs, duplicated integrations, and limited oversight.

AI governance and operations services built for controlled adoption

AI Ops, Governance & Gateway

AI governance framework

A practical governance model for managing AI usage across teams, systems, and workflows.

It includes: Responsible AI policies, AI usage policies, ownership models, risk classification, approval workflows, human review points, model risk management, compliance readiness, and AI policy and guardrails.

AI Ops, Governance & Gateway

LLM gateway implementation

A centralized gateway layer to control how users, applications, agents, tools, and models interact.

It includes: Model routing, access control, authentication, rate limits, usage policies, provider management, agent permissions, secure system access, cost controls, and vendor flexibility.

AI Ops, Governance & Gateway

LLMOps and AgentOps

Production operations for LLM applications, AI agents, and AI-powered workflows after launch.

It includes: Deployment workflows, prompt and model versioning, evaluation pipelines, fallback handling, release governance, incident tracking, production support, and continuous improvement.

AI Ops, Governance & Gateway

LLM observability and audit logging

Visibility into how AI systems behave, what they cost, and where risk or performance issues appear.

It includes: Prompt and response monitoring, usage analytics, cost tracking, latency monitoring, hallucination risk checks, audit logs, performance monitoring, model behavior tracking, and workflow-level reporting.

AI Ops, Governance & Gateway

AI compliance readiness

Governance support for internal policies, customer requirements, industry standards, and emerging AI regulations.

It includes: AI system inventory, control mapping, documentation, audit trail design, risk review workflows, human oversight models, EU AI Act readiness, and compliance reporting support.

AI Ops, Governance & Gateway

AI policy and guardrails

Practical controls that define what AI systems can do, what they can access, and when humans need to stay involved.

It includes: Sensitive data rules, restricted actions, prompt injection safeguards, output review rules, escalation paths, agent permission boundaries, approved use case policies, and exception handling.

Use cases: real value, real scenarios

Control layer before AI sprawl

We help you put access rules, gateway controls, logging, and ownership in place before AI usage spreads across disconnected tools and teams.

Governance designed around real workflows

We shape AI controls around where AI actually acts, from support and finance to operations, HR, sales, and internal systems.

Engineering-led governance

Our AI, data, cloud, security, and integration teams make governance executable across APIs, identity, monitoring, deployment, and business systems.

Cost and vendor flexibility built in

We design AI environments that support usage tracking, model routing, rate limits, and provider flexibility so teams can scale without hidden cost or lock-in.

How we build the control layer your AI systems need

01

01 AI usage assessment

We start by mapping where AI is already being used across your teams, tools, workflows, models, and vendors. This includes internal copilots, LLM applications, AI agents, APIs, automation workflows, and any shadow AI usage that may not have formal oversight.

Deliverables: AI usage inventory | Risk and access assessment | Governance gap report

02 Governance blueprint

Next, we define the governance model needed to manage AI responsibly across business workflows. This includes ownership, approved use cases, risk tiers, human review points, policy requirements, audit needs, and guardrails for sensitive data and restricted actions.

Deliverables: AI governance framework | Policy and guardrail model | Risk classification structure

03 Gateway architecture

We design the LLM gateway layer that controls how users, applications, agents, tools, and model providers interact. This creates a centralized control point for access, authentication, model routing, rate limits, logging, cost visibility, and provider flexibility.

Deliverables: LLM gateway blueprint | Access control model | Model routing and provider strategy

04 LLMOps and observability setup

We implement the operating layer needed to monitor and manage AI systems after launch. This includes prompt and response monitoring, evaluation workflows, audit logs, cost tracking, latency tracking, fallback handling, incident workflows, and model behavior monitoring.

Deliverables: Observability dashboards | Evaluation workflows | Audit logging and monitoring setup

05 Production governance and optimization

Once the AI control layer is live, we help teams manage ongoing performance, cost, risk, and compliance readiness. Governance rules, model usage, guardrails, and observability workflows are reviewed and improved as AI adoption expands.

Deliverables: AI operations playbook | Cost optimization plan | Continuous governance roadmap

How we build the control layer your AI systems need

step 1

AI usage assessment

Deliverables: AI usage inventory | Risk and access assessment | Governance gap report

step 2

Governance blueprint

Deliverables: AI governance framework | Policy and guardrail model | Risk classification structure

step 3

Gateway architecture

Deliverables: LLM gateway blueprint | Access control model | Model routing and provider strategy

step 4

LLMOps and observability setup

Deliverables: Observability dashboards | Evaluation workflows | Audit logging and monitoring setup

step 5

Production governance and optimization

Deliverables: AI operations playbook | Cost optimization plan | Continuous governance roadmap

Build your AI control layer the right way from the start

We assess your current AI environment, design the governance model, and implement the control layer your teams need to operate AI at scale.

Tool & technologies

Microsoft Purview

Informatica

Collibra

Success stories in spotlight

60% reduced customer response times with an AI assistant

Hospitality & TravelArtificial Intelligence

View case study

WATCH TESTIMONIAL

Don Sgouridis

CIO, BBJ Latavola

50% faster discovery with an AI-powered knowledge sharing agent

AgricultureArtificial Intelligence

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"I’m going over testing feedback about the chatbot; overall, testers found the product to be fantastic. We’re very happy with this."

Chris Head

Director of IT, CCOF

GreenGro cuts support time in half with AI knowledge agent

AgricultureArtificial Intelligence

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"tkxel built an AI assistant that delivers accurate guidance fast and reduces manual work."

Director of Operations

Engineering the architecture behind intelligent content creation

SaaS & TechnologyArtificial Intelligence

View case study

"tkxel built an AI content platform that streamlined workflows, improved team alignment, and boosted productivity."

Head of Product

Embedding agentic AI in Slack to eliminate workflow bottlenecks

Media & EntertainmentArtificial Intelligence

View case study

"tkxel built a Slack AI assistant that surfaces SOPs instantly and keeps our team productive."

Head of Operations

Powering enterprise growth with 10x faster insights and scalability

SaaS & TechnologyArtificial Intelligence

View case study

"tkxel optimized our AI platform to scale faster, improve reliability, and control cloud costs."

VP of Engineering

54% reduction in reporting inconsistencies via governed KPIs and automated lineage tracking.

SaaS & TechnologyData GovernanceKPI DefinitionSemantic Modelling

Governance framework + BI standardisation

“tkxel brought clarity to our reporting. Our teams now rely on one source of truth rather than debating which numbers to trust.”

Director of Analytics

3× faster analytics delivery after migrating to a modern cloud warehouse with automated ELT pipelines.

SaaS & TechnologyCloud data architectureData ModellingETL/ELT Pipelines

Data warehouse modernisation + BI automation

"Thanks to tkxel, we moved from daily spreadsheet chaos to real-time analytics. Our merchandising and ops teams finally have accurate insights."

VP Data & Insights

41% improvement in compliance readiness with centralised lineage and access governance.

SaaS & TechnologyCompliance FrameworksData LineageMDM

Regulatory-grade governance implementation

“Their governance implementation gave us audit-ready controls and eliminated the risk of inconsistent regulatory reporting.”

Head of Data Governance

67% faster dashboard performance through semantic layer optimisation and metric standardisation.

SaaS & Technologydashboard optimisationKPI modellingsemantic layer design

BI performance optimisation

“tkxel helped us fix long-standing BI bottlenecks. Dashboards load instantly, and every team works with consistent definitions.”

Director of Business Intelligence

60% reduction in manual reconciliation work by automating data validation across core systems.

SaaS & TechnologyData PipelinesData quality managementMonitoring frameworks

Data quality automation + BI reliability

“We no longer waste hours fixing broken reports. tkxel automated our validation workflows and improve trust across the organisation.”

Head of Data Operations

What you can achieve with governed AI adoption

Clear AI ownership and governance

Know who owns each AI system, which workflows it supports, what risks it carries, and where human review is required. This helps teams move from scattered AI usage to structured, accountable adoption.

Safer access across models, agents, and systems

Control what users, applications, and AI agents can access across models, tools, files, APIs, and business systems. This reduces the risk of sensitive data exposure, unauthorized actions, and unmanaged AI behavior.

Better visibility into AI performance

Monitor prompts, responses, costs, latency, errors, hallucination risk, and agent actions in one place. This gives teams the clarity needed to debug issues, improve reliability, and trust AI systems in production.

Stronger cost and vendor control

Track LLM usage by team, workflow, model, or application. Use gateway controls, routing logic, rate limits, and provider flexibility to reduce waste and avoid overdependence on a single AI vendor.

Improved compliance readiness

Build the policies, audit trails, documentation, risk classifications, and human oversight needed to support internal reviews, customer requirements, and emerging AI regulations.

See where your AI control gaps are

Consult for free

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TOP 100 INSPIRING WORKPLACES 2025

FORBES COACHES COUNCIL

FINANCIAL TIMES

mogul people leader

ISO 27001 CERTIFIED

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CMMI DEV 3 CERTIFIED

Start building your AI control layer today

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Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

“Tkxel has proven beyond par that they excel not just in building and integrating with our team but building at a level that is at par with any US development team. Working with Tkxel is one of the best decisions we have made.”

Umair Bashir

CTO, Replenium

“tkxel shared our vision right from the get go, and helped us achieve the unthinkable through perseverance and a thorough attention to detail. Their team was highly professional and possessed a firm grasp on technicalities, a combination that is hard to find in the industry.”

Pam Chitwood

Product Manager, ABB

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Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

Umair Bashir

CTO, Replenium

Pam Chitwood

Product Manager, ABB

Frequently asked questions

What is AI Ops, Governance & Gateway?

AI Ops, Governance & Gateway is the operating layer for controlled AI adoption. It brings together an AI governance framework, LLMOps services, LLM observability, LLM gateway implementation, AI policy & guardrails, and cost controls so teams can manage AI usage safely in production.

Why does our business need an AI governance framework?

An AI governance framework defines how AI systems are approved, owned, monitored, and reviewed. It helps reduce shadow AI, clarify accountability, manage model risk, and create consistent rules for data access, human review, and responsible AI use.

What is an LLM gateway, and how does it help?

An LLM gateway is a control layer between users, applications, agents, tools, and model providers. LLM gateway implementation helps manage model routing, authentication, logging, rate limits, token/cost controls, fallback handling, and provider flexibility.

How do LLMOps services support production AI?

LLMOps services help teams manage LLM applications after launch. This includes prompt and model versioning, evaluations, deployment workflows, model monitoring, fallback handling, incident tracking, and continuous performance improvement.

What does LLM observability include?

LLM observability gives teams visibility into prompts, responses, latency, errors, costs, usage patterns, retrieval quality, hallucination risk, and agent actions. Tools like Langfuse and LangSmith can support tracing, debugging, evaluation, and monitoring across AI workflows.

How do AI policy & guardrails reduce risk?

AI policy & guardrails define what AI systems can access, what actions they can take, which use cases are approved, and when humans need to review outputs. They can also help reduce risks such as prompt injection, sensitive data exposure, unsafe responses, and unauthorized agent actions.

Can tkxel help with AI compliance services and EU AI Act compliance?

tkxel can support AI compliance services by helping teams create AI inventories, risk classifications, audit trails, documentation, oversight workflows, and control mapping. For the EU AI Act, we support compliance readiness, while legal interpretation or certification should involve qualified legal counsel.

Do you align with frameworks like NIST AI RMF and ISO/IEC 42001?

Yes. Where relevant, tkxel can align responsible AI consulting and model risk management work with frameworks such as NIST AI RMF and ISO/IEC 42001. These frameworks help guide risk management, governance, accountability, monitoring, and continuous improvement.