Healthcare processes,redesigned around AI.
Prior authorisation, payment integrity and clinical policy, rebuilt by people who ran payer and provider operations. Agents do the reading; clinicians make every call. One reviewer went from one guideline a day to ten.
Who we work with.
Domain, process and AI, applied to named problems.
We start in healthcare, where we have run the operations ourselves, and bring three things to every problem: domain expertise, process fluency and AI enabled execution. The result is a business process that runs differently and a number that moved.
Healthcare & health plans
Prior authorisation, payment integrity, clinical policy, care gaps, member service.
Explore →Banking, financial services & insurance
Procurement decisions, KYC refresh, claims evidence, underwriting files.
Explore →Retail & distribution
Spend categorisation, vendor normalisation, pricing and demand forecasting.
Explore →ESG & sustainability
Disclosure data with its source attached, on a governed customer data foundation.
Explore →Domain expertise
Every practice lead has run the process we are changing: prior authorisation queues, payment integrity, procurement, disclosure. We know where the work actually gets stuck.
Explore →PillarProcess fluency
We redesign the process around the decision, not the tool. Who reads, who judges, who signs, what escalates. Twelve workflows, each with a named owner and a baseline.
Explore →PillarAI enabled execution
Agents take the reading, matching and drafting; people keep every judgment. Each agent starts in shadow mode and earns autonomy on evidence.
Explore →FoundationAccelerators and process playbooks
What worked in one workflow is codified so the next starts ahead: specs, evals, guardrails and the questions a regulator will ask.
Explore →FoundationData foundations and master data
One trusted customer record on Microsoft Fabric and Profisee. The foundation the process transformation depends on.
Explore →FoundationAI cost and usage control
Argos shows what your AI costs by process and outcome, and stops usage nobody approved. A cost line finance can read.
Explore →How an engagement runs.
AIM is the way we engage: assess where you stand, implement one named problem to a measured outcome, then mature it until your team runs it. Delivered by one accountable team, as a project or as a longer term capability centre.
AIM
Assess, Implement, Mature. You don't build until you've assessed; nothing is mature until it measures its own outcome and cost.
See the phases → DeliveryHybrid pods
Our operators, your experts, our agents. Agents take the volume; people take the judgment and sign.
How pods run → EngineeringVoyager
Every deploy gated on behaviour, safety and cost evals. A named approver is required to override.
Inside Voyager → OperationsArgos
Every model call metered at the gateway and attributed to a project, a use case and an outcome.
Inside Argos →Agents we have built and can deploy.
Grouped by the AIM phase they run in. Each one starts in shadow mode and earns autonomy on evidence.
Portfolio Scanner
Finds every AI initiative running in your estate, including the unreported ones.
BoundedAssessSpend Archaeologist
Reconstructs what you really spend on AI, across cost centres and vendors.
BoundedImplementData Quality Sentinel
Scores incoming data against quality rules and quarantines records that would pollute the golden record.
BoundedImplementCustomer Match & Survivorship
Resolves duplicate customer records and proposes the golden record, field by field.
ApproveMatureArgos Token Sentinel
Meters every model call and alerts before a budget breaks.
BoundedMatureDrift & Quality Monitor
Watches production outputs for drift and bias and opens an incident when the numbers move.
BoundedEvery engagement started with a number someone owned.
Clinical policy into payment rules
$5M+ contingent revenue in six months; 60+ reviewers adopted it with no mandate.
Read the case study →43 pilots into one governed portfolio
Stopped 19, merged 14 into 6, accelerated 10. 37% measured contribution to FY24 objectives.
Read the case study →Spend under intelligence
Manual purchase review replaced by a model that learns from every override.
Read the case study →The $56M discovery
Asked to validate a $40M target. $31M realised in year one.
Read the case study →Care and coverage, one evidence layer
Prior auth, care gaps and contact centre on one governed platform.
Read the case study →Vendor neutral. Built on what you already run.
Your cloud, your region, your models. These are the platforms our teams work in every day.
What you get that a strategy deck or a staffing contract doesn't.
Every practice lead has run the systems we now automate, inside the kinds of organisations we work with today.
A base fee that covers the pod, and an outcome component that pays only when the number moves and Finance agrees it moved.
Model cards, validation evidence, fairness monitoring and a full audit trail ship with the build.
Specs, evals, pipelines and patterns are yours. We measure ourselves on how quickly your team runs it without us.
Read how we think before you talk to us.
Why your AI bill will look like your 2015 cloud bill
Token spend is growing faster than anyone budgeted. The fix is the one FinOps already found.
Read the blog → BlogThe 43-pilot problem
How to stop AI projects without stopping AI: what to stop, merge and accelerate.
Read the blog → White paperThe AI Maturity Model
Why most enterprises that think they are Level 2 are not yet Level 1, and what to do about it.
White papers →Frequently asked questions about 8thElement
What exactly does 8thElement do?
We are an AI implementation and operations firm. We put a hybrid pod, our operators, your domain experts and our agents, inside one of your workflows, take it from stalled pilot to production, and govern it afterwards. The work is sequenced by our AIM framework, built through Voyager, and metered by Argos. Our fee is tied to the business number the sponsor owns.
How is this different from a consultancy or a system integrator?
A consultancy writes the recommendation and leaves. An integrator bills for time whether or not the number moves. We do both jobs in one accountable team and put a majority of the fee at risk against a baseline your Finance function signs before we start. We also build to hand over: specs, evals, pipelines and patterns stay with your engineers.
Who uses 8thElement?
Enterprises in regulated, high volume industries: health plans and health systems, payment integrity and healthcare technology companies, sustainability data providers and corporate ESG teams, banks and insurers, retailers and distributors, and life sciences organisations. Inside those, our sponsors are typically the executive who owns a cost line or a cycle time, not the innovation team.
What are the agents actually allowed to do?
Every agent starts in shadow mode, where it produces outputs and humans decide exactly as before. It moves to approve mode, recommending, with a named person deciding, only on eval evidence, and to bounded autonomy only within thresholds your risk owner sets. Adverse decisions about a person are never issued by an agent, in any mode. All 41 agents publish their inputs, guardrails and the KPI they are measured on.
How do you handle PHI, PII and data residency?
We operate inside your compliance boundary. Deployments run in your VPC, in your region, against models you have approved. Regulated data classes are tagged and Argos blocks any route to an unapproved model or region. We are SOC 2 Type II, aligned to HIPAA with BAA capability, GDPR ready, and aligned to SR 11-7 and the NIST AI RMF.
What does AI actually cost to run, and how do you prove it?
Argos meters every model call at the gateway and attributes it to a project, use case, team and outcome, metadata only, never prompt content. You get spend against budget, an updated hourly month end forecast, showback to cost centres, and cost per outcome rather than cost per token. That last number is what our outcome fee is read from.
How long before we see something in production?
Twelve weeks to a first production outcome is our median, assuming a prioritised use case, access to the relevant systems and a sponsor who owns the number. Blueprint and eval scaffolding land by week two, shadow mode by week eight, approve mode with a measured result by week twelve. A full Assess phase, if you need one first, is twelve weeks on its own and the fee is credited against Implement.
How do we start?
Two ways. Take the two minute maturity check to see which AIM phase fits where you are, or book a 45-minute working session with an operator. No deck and no credentials tour, bring the queue, the backlog or the cost line, and we will tell you honestly whether we can move it.