Managed Services

We run your
operations.

ColdAI runs business operations for organizations that would rather buy a working, improving function than build the capability themselves. Across twelve domains, from finance and procurement to insurance, health and trust and safety, AI agents take on high-volume, rules-heavy work while dedicated people handle judgment, exceptions and accountability. Every engagement follows six phases, from Discover to Optimize, so improvement continues after the service goes live.

Operations Areas

12 domains. One integrated operating model.

Finance Operations

Finance Operations

End-to-end financial process management — from accounts payable and receivable automation to real-time treasury analytics, regulatory reporting, and AI-driven forecasting that transforms finance from a cost center into a strategic advantage.

Supply Chain

Supply Chain

Intelligent supply chain orchestration powered by predictive demand sensing, autonomous inventory optimization, and real-time visibility across multi-tier supplier networks spanning global logistics corridors.

Sourcing & Procurement

Sourcing & Procurement

Strategic sourcing and procurement transformation through AI-powered vendor evaluation, automated contract lifecycle management, spend analytics, and dynamic supplier risk scoring across complex enterprise ecosystems.

Digital Engineering & Manufacturing

Digital Engineering & Manufacturing

Converging digital engineering with smart manufacturing — deploying digital twins, AI-driven quality control, predictive maintenance, and Industry 4.0 production systems that optimize yield and reduce downtime.

Talent and HR

Talent and HR

Modern talent operations encompassing AI-augmented recruitment, workforce analytics, employee experience platforms, skills mapping, and organizational design that attracts and retains high-caliber teams.

Marketing Services

Marketing Services

Data-driven marketing operations covering campaign orchestration, marketing automation, customer analytics, content personalization engines, and attribution modeling that connects every touchpoint to business outcomes.

Customer Service Operations

Customer Service Operations

Next-generation customer service delivery through AI-powered contact centers, intelligent routing, sentiment analysis, omnichannel engagement platforms, and self-service automation that scales with demand.

Banking

Banking

Core banking operations management including transaction processing, payment infrastructure, digital lending platforms, KYC/AML compliance automation, and real-time fraud detection systems for financial institutions.

Insurance

Insurance

Insurance operations transformation spanning claims processing automation, underwriting intelligence, policy administration, actuarial analytics, and customer portal modernization for carriers and MGAs.

Health

Health

Healthcare operations management covering clinical workflow optimization, revenue cycle management, patient engagement platforms, population health analytics, and regulatory compliance across provider networks.

Risk and Compliance

Risk and Compliance

Enterprise risk and compliance management through automated regulatory monitoring, continuous control testing, risk quantification models, audit trail systems, and governance frameworks across jurisdictions.

Trust & Safety

Trust & Safety

Digital trust and safety operations including content moderation at scale, fraud prevention systems, identity verification, platform integrity monitoring, and AI-powered threat detection for digital ecosystems.

In depth

Contracting for and running an AI managed service

Five working guides for buyers: how to measure, price, transition, regulate and govern a service in which software does part of the work.

  1. 01GuideWriting SLAs for AI-run business operationsHow to write an SLA for an AI-assisted managed service: indicators per process, sampled quality checks, error budgets, credits and rules for model changes.
  2. 02ComparisonFTE, transaction or outcome pricing for managed servicesCompare FTE, fixed-fee, per-transaction and outcome-based pricing for managed services, the incentives each creates once AI does the work, and how to contract.
  3. 03ProcessTransitioning operations to a managed serviceA step-by-step plan for moving a business function to a managed service: discovery, design, knowledge transfer, parallel running, cutover and the exit plan.
  4. 04Regulation explainerDORA and EBA rules for outsourced operationsWhat DORA, the EBA and PRA outsourcing rules and HIPAA require in contracts and oversight when a bank, insurer or health organization outsources operations.
  5. 05ChecklistManaged service governance: a working checklistA working governance checklist for a managed service: three review tiers, decision rights, change control for AI model and prompt changes, and exit readiness.

What changes when AI takes on part of an operation

Traditional outsourcing moved work somewhere cheaper, with roughly the same number of people following roughly the same steps. An AI-enabled managed service changes how the work is done. Software agents read documents, match records, draft replies and route cases; people take the items the software cannot settle and the decisions that need human accountability. The provider runs both, and the boundary between them is part of the service design.

That shifts what a buyer should examine. Headcount and location matter less. The questions become which decisions the software makes alone, how its output is checked, how changes to models and prompts are controlled, how the price responds as automation grows, and what happens when something goes wrong. Those are contract and governance questions as much as technical ones, which is why the in-depth guides on this page cover service levels, pricing, transition, regulation and governance.

Managed Services is the right offer when you want a function run for you. If you would rather own and operate the system yourselves, that is a development engagement with a handover instead1.

The six phases as a loop rather than a line

Redesign from evidence01Discover02Design03Build04Migrate05Operate06Optimize
  1. Discover

    Measure volumes, exceptions and the cost of the current process, and record the baseline the service will be judged against.

  2. Design

    Decide which steps software performs and which people keep, with the controls and service levels around both.

  3. Build

    Configure agents, integrations and quality checks, and test them on real historical cases rather than tidy samples.

  4. Migrate

    Move live work through shadowing, a parallel run and cutover, against acceptance criteria agreed in advance.

  5. Operate

    Run the service day to day with sampled quality checks, named escalation and regular service reviews.

  6. Optimize

    Use root-cause data, process mining and benchmarking to find the next improvement and feed it back into design.

Conceptual view of ColdAI's six-phase managed-service model, in which Optimize feeds back into Design. It is not a timeline.

Twelve domains: what software takes on and what people keep

The twelve operations areas group into six families with similar patterns. The split below is illustrative; each service starts from the client's own process and risk appetite.

Domain familyWork AI agents can take onDecisions people keepConstraint to design around
Finance, sourcing and procurementInvoice capture and coding, matching, reconciliations, spend classification, supplier onboarding checksPayment approvals above limits, write-offs, supplier disputes, contract awardsSegregation of duties and an audit trail for every financial control
Supply chain, digital engineering and manufacturingDemand-signal monitoring, order exception handling, inspection triage, drafting maintenance work ordersAllocation when supply is short, product release, safety-related maintenance decisionsData from many partners and plant systems of uneven quality
Talent, HR and marketing servicesScreening support, HR case triage, campaign operations, content tagging and reportingHiring and dismissal, pay, brand and claims approvalEmployment law; in the EU, AI used in recruitment is listed as high-risk under the AI Act, Annex III3
Customer service and trust and safetyIntent detection, drafted replies, routing, triage of reports and content, review of fraud signalsComplaints with regulatory deadlines, account closures, enforcement on borderline contentConsumer protection rules, platform obligations and limits on reviewer exposure
Banking and insuranceKYC file assembly, alert triage, claims intake and document checks, policy servicingCustomer risk ratings, suspicious activity reports, claim denials, underwriting exceptionsOutsourcing and ICT third-party rules, model risk management and fair treatment of customers
Health, risk and compliancePreparing prior authorization packets, coding support, collecting control-testing evidence, monitoring regulatory changeClinical decisions, coverage determinations, control sign-off and attestationsPatient data protection (HIPAA in the US, GDPR in the EU) and clinical safety

Where a domain is regulated, the constraint column usually decides the shape of the contract before the technology is chosen.

Is this function ready to be run as a managed service?

  • If

    The work is high in volume, follows written rules most of the time and has a clear output

    Then

    It is a strong candidate; begin discovery by measuring volumes, cycle times and error rates

    That baseline is what every later service level and price will rest on.

  • If

    Most of the value comes from judgment only your own staff can exercise

    Then

    Keep those decisions in-house and consider handing over only the preparation work around them

    Outsourcing judgment you cannot specify leaves nobody able to check it.

  • If

    Nobody can describe how the process runs today, or it differs by site

    Then

    Map and standardize it first, with process mining where system logs exist

    Outsourcing an undocumented process moves the confusion rather than removing it.

  • If

    The function supports a critical or important regulated activity

    Then

    Map your outsourcing and ICT third-party obligations before scoping

    They decide audit, exit and data terms that the service design has to meet.

  • If

    You want to own and change the system yourselves in the long run

    Then

    Commission a build with a handover, or a managed service with a planned insourcing date

    Planning the exit up front is far cheaper than negotiating it later.

Five ways an AI-run service goes wrong, and where each is covered

Automating a broken process

Early signalHigh exception rates from the first week of live running.

MitigationFix or standardize during Design, and use process mining to see where the variants come from.

Quality drift that nobody sees

Early signalSpeed measures hold while rework or complaints rise.

MitigationMake sampled human QA a contractual indicator, as set out in writing SLAs for AI-run operations.

Incentives that work against automation

Early signalThe provider's revenue rises with headcount.

MitigationUse unit or outcome pricing with a quality gate; compare the options in FTE, transaction or outcome pricing.

Knowledge locked inside the provider

Early signalNobody in-house can explain how the service makes its decisions.

MitigationKeep retained roles, current documentation and exit readiness on the governance agenda; see the governance checklist.

How ColdAI runs a managed service day to day

Each service combines dedicated teams with intelligent automation and AI-driven analytics, and runs against agreed service levels once it reaches Operate. Improvement does not stop at go-live: the Optimize phase uses process mining and performance benchmarking to find the next change, which is then designed, tested and released like any other.

ColdAI's comparison pages describe its managed operations as run by an AI agent operations team, with pricing based on outcomes rather than on the number of people assigned2. Specific service levels and prices are set per engagement from the baseline measured in discovery; we do not publish standard figures, because a target that ignores your process's starting point would not mean much.

Frequently asked questions

What is an AI managed service?

It is an arrangement in which a provider runs a business function for you and uses AI agents to do part of the work, with people handling exceptions, judgment and accountability. You buy the operating result, measured through service levels, rather than software licences or staff hours, and the provider remains responsible for improving the service over time.

Which parts of an operation should stay in-house?

Decisions that define your risk appetite or need your own accountability, such as credit policy, clinical judgment or final complaint outcomes, plus a small retained team: a service owner, process experts and control owners. Preparation, routine processing and evidence gathering around those decisions are usually the work that moves to the service.

Do we remain accountable for decisions the service makes?

Yes. Outsourcing moves the work, not the accountability, and in regulated sectors the law says so explicitly. That is why the contract should give you audit access, change approval for anything that alters outcomes, incident notification and an exit plan, and why your governance forums should review quality samples rather than only speed.

Can we start with one process rather than a whole function?

Yes, and it is often the better route. A single, well-measured process such as invoice processing or customer support triage gives both sides a baseline, a working governance rhythm and evidence about automation coverage. Further processes can then be added through the same six phases with far less uncertainty.

What happens to our current team when an operation moves to a managed service?

It depends on the design and the jurisdiction. Some people move to the provider, sometimes under staff-transfer law such as TUPE in the UK; some form the retained team that oversees the service; some move to other roles. Consultation should be honest about how automation is expected to change the work over time.

How is a ColdAI managed service priced?

On outcomes rather than per full-time-equivalent person, as our comparison pages state. The exact structure, such as a base fee, unit pricing or a gainshare, is agreed per engagement once discovery has produced a baseline. We do not publish price lists, because the starting point of each process differs too much.

Sources

  1. Software development: when to build, hand over or have it run for you — ColdAI
  2. ColdAI vs Accenture: managed operations and pricing model — ColdAI
  3. Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) — EUR-Lex
Our Methodology

How we develop and manage services

A proven six-phase methodology that takes operations from assessment through continuous optimization — delivering measurable outcomes at every stage.

01

Discover

Deep operational assessment and process mapping to identify transformation opportunities.

02

Design

Solution architecture and service model design tailored to your operational requirements.

03

Build

Rapid implementation of automation, AI models, and integrated operational platforms.

04

Migrate

Seamless transition of operations with zero-downtime cutover and knowledge transfer.

05

Operate

Continuous managed service delivery with dedicated teams and SLA-backed performance.

06

Optimize

Ongoing improvement through AI analytics, process mining, and performance benchmarking.

Ready to transform your operations?

Let us assess your current operational landscape and design a managed services engagement.