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Is this your company? Claim itWe make AI software that reads hospital documentation and helps coding and controlling teams capture diagnoses and procedures correctly.
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We ship AI replies that pull from each customer's own knowledge base, and right now our engineers judge draft quality by hand. It doesn't scale. We need a lightweight way to score retrieval and answer quality on the customer's own content before replies reach end users. No dedicated ML staff to run it, and pricing must be predictable.
We keep our data pipelines reliable while product and analytics demand keeps growing, and it is a constant struggle. A good solution must fit our cloud warehouse and orchestration setup, surface failures early with clear lineage, and give us an audit trail. We will trial it with a small team first.
We serve customers in several European countries, and each imposes different rules on the same clinical record data. Today we track policies per customer through manual review, which is slow and error-prone. A good solution must show consistent lineage, map rules to each national customer, run on premises and in public cloud, and rely on open standards.
We can't tell how teams actually adopt our product or where they drop off before converting. Our data lives in a cloud warehouse, and we're building the first real analytics layer on top of it. A good solution must plug into that warehouse without moving data around, show adoption and conversion by team, and stay lightweight and startup-friendly on price.
We run a platform where brokers design and manage their own insurance products, so routine requests and policy changes keep piling up. We want AI helpers that handle the repetitive ones without adding headcount. A good solution must understand insurance regulation, plug into our policy system, keep humans in control of changes, and show clear pricing.
Our payment integrity rules live inside our own claims engine, and overpaid or duplicated claims often get caught only after money has gone out. We want detection that flags them before payment, learns beyond hand-coded rules, and plugs into our in-house system without a rebuild. It must handle protected health data under a signed business associate agreement.
We run machine learning models for pricing and member engagement, deployed as services by data engineers, and checks before release are left to each team. We want one disciplined evaluation routine that works alongside actuarial staff, keeps an audit trail of model decisions, and runs inside our own cloud tenancy. Insurance references matter.
We wish there were a connector layer that takes consult notes from our app and writes them into each partner hospital's records system without custom work every time. We would judge it by how little setup a new hospital needs, whether clinicians can read and correct what gets synced, and whether every write leaves an audit trail.
We publish claims about how coding agents behave on messy, real-world code, and today we rerun our benchmarks by hand each time a new model ships. It eats research time and results drift. We need repeatable runs we can trigger per model release, with comparable scores over time, light enough for a small team and modest budget.
Late or bad data quietly skews our credit models, and we usually find out after the damage is done. Today validation lives inside individual feature jobs, so coverage is patchy. We want checks and alerts across the whole warehouse, with lineage analysts can follow, working alongside our current warehouse and orchestration tools, and pricing that stays predictable as volume grows.
We wish for evaluation tooling built for regulated advice. It would score our model's explanations of portfolio decisions against clear criteria, flag unsupported claims, and route doubtful outputs to a human reviewer with an audit trail. We would judge it by how fast a small team can trial it, how well it catches errors staff review finds, and whether it never trains on our data.
Our claims and fraud handlers keep repeating the same manual lookups across systems, which eats time better spent on judgement. We want agents that automate those lookups safely, with clear audit trails, human approval on sensitive actions, and tight access controls. Insurance experience matters. Who has handled this well in regulated claims environments?
We extract many variables from clinical notes, and each one needs a clinician to check it before we share data with research partners. By hand, that review is slow and eats our small team's time. A good solution must flag likely errors, sample smartly, track reviewer decisions, run inside hospital walls, and stay affordable for us.
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