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.
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.

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.

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.

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.

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.

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

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

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.

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

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

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

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
Five working guides for buyers: how to measure, price, transition, regulate and govern a service in which software does part of the work.
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.
Measure volumes, exceptions and the cost of the current process, and record the baseline the service will be judged against.
Decide which steps software performs and which people keep, with the controls and service levels around both.
Configure agents, integrations and quality checks, and test them on real historical cases rather than tidy samples.
Move live work through shadowing, a parallel run and cutover, against acceptance criteria agreed in advance.
Run the service day to day with sampled quality checks, named escalation and regular service reviews.
Use root-cause data, process mining and benchmarking to find the next improvement and feed it back into design.
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 family | Work AI agents can take on | Decisions people keep | Constraint to design around |
|---|---|---|---|
| Finance, sourcing and procurement | Invoice capture and coding, matching, reconciliations, spend classification, supplier onboarding checks | Payment approvals above limits, write-offs, supplier disputes, contract awards | Segregation of duties and an audit trail for every financial control |
| Supply chain, digital engineering and manufacturing | Demand-signal monitoring, order exception handling, inspection triage, drafting maintenance work orders | Allocation when supply is short, product release, safety-related maintenance decisions | Data from many partners and plant systems of uneven quality |
| Talent, HR and marketing services | Screening support, HR case triage, campaign operations, content tagging and reporting | Hiring and dismissal, pay, brand and claims approval | Employment law; in the EU, AI used in recruitment is listed as high-risk under the AI Act, Annex III3 |
| Customer service and trust and safety | Intent detection, drafted replies, routing, triage of reports and content, review of fraud signals | Complaints with regulatory deadlines, account closures, enforcement on borderline content | Consumer protection rules, platform obligations and limits on reviewer exposure |
| Banking and insurance | KYC file assembly, alert triage, claims intake and document checks, policy servicing | Customer risk ratings, suspicious activity reports, claim denials, underwriting exceptions | Outsourcing and ICT third-party rules, model risk management and fair treatment of customers |
| Health, risk and compliance | Preparing prior authorization packets, coding support, collecting control-testing evidence, monitoring regulatory change | Clinical decisions, coverage determinations, control sign-off and attestations | Patient 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.
The work is high in volume, follows written rules most of the time and has a clear output
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.
Most of the value comes from judgment only your own staff can exercise
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.
Nobody can describe how the process runs today, or it differs by site
Map and standardize it first, with process mining where system logs exist
Outsourcing an undocumented process moves the confusion rather than removing it.
The function supports a critical or important regulated activity
Map your outsourcing and ICT third-party obligations before scoping
They decide audit, exit and data terms that the service design has to meet.
You want to own and change the system yourselves in the long run
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.
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.
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.
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.
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.
Early signalThe contract lacks audit, data-return or exit terms.
MitigationMap obligations before signing using the guide to DORA and outsourcing rules, and plan the move with the transition plan.
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.
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.
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.
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.
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.
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.
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
A proven six-phase methodology that takes operations from assessment through continuous optimization — delivering measurable outcomes at every stage.
Deep operational assessment and process mapping to identify transformation opportunities.
Solution architecture and service model design tailored to your operational requirements.
Rapid implementation of automation, AI models, and integrated operational platforms.
Seamless transition of operations with zero-downtime cutover and knowledge transfer.
Continuous managed service delivery with dedicated teams and SLA-backed performance.
Ongoing improvement through AI analytics, process mining, and performance benchmarking.
Let us assess your current operational landscape and design a managed services engagement.