Problems shaped
like this.
Written as problem statements and the approach we would take, not as customer claims. Each one carries what a conventionally staffed team takes to develop and deploy it, against what the agents take, and what set the pace in each case. If one of these is your problem, the intake conversation starts from here.
Each lists the constraints that would drive its complexity tier. Those four - compliance, deployment target, scale and latency, are the same signals ChiefManager™ extracts from a conversation and prices against.
Surface defect inspection that keeps up with the line
Defect classes are known, but inspection is manual, inconsistent between shifts, and slows the line whenever it is taken seriously.
Escapes reach customers. Scrap is discovered a shift late, when the whole batch is already wrong.
- Train on the defect images you have already labelled
- Run inference on an edge device at the line, not in a datacentre
- Emit pass/fail to the MES so the line can act without a human in the loop
- Retrain from new defect classes without a vendor engagement
Camera placement and calibration per line, done on site.
Machine downtime predicted before it stops the line
Sensor history exists across PLCs and historians, but nobody has turned it into a warning anyone acts on.
Unplanned downtime is discovered when the line stops. Maintenance runs on a calendar, not on condition.
- Ingest historian and PLC telemetry into one time-series store
- Model failure signatures per asset class rather than one global model
- Raise work orders into the CMMS with the evidence attached
- Track prediction quality against what actually failed
Historian data was already centralised, so nothing waited on plant access.
Changeover and OEE loss nobody can currently attribute
OEE is reported weekly from manual logs, so the losses are known in aggregate and never at the point they happened.
Improvement projects are argued from anecdote. The same changeover loses the same twenty minutes every week.
- Derive state, running, idle, changeover, blocked, from existing cameras
- Attribute every minute of loss to a cause and a station
- Publish OEE that reconciles to what the line actually did
Line-side cameras to mount, but one cell proves the approach before the rest.
PPE and restricted-zone compliance across multiple sites
Cameras record everything and surface nothing. Breaches are found in review, after someone was already exposed.
Near-misses go unrecorded until one is not a near-miss. Audit evidence is assembled by hand after the fact.
- Ingest existing RTSP streams without replacing hardware
- Detect zone entry, unsafe proximity, missing PPE and falls
- Push events into the incident system already in use
- Retain the evidence clip against each finding for audit
Air-gapped, one building at a time, commissioned by someone physically there. The least compressible shape there is.
Goods-in reconciliation that stops paying for what never arrived
Delivery notes, purchase orders and what physically arrived are reconciled by a person comparing three screens.
Short deliveries are paid for. Disputes are unwinnable weeks later because nobody photographed the pallet.
- Read delivery documents at the dock, whatever format they arrive in
- Match against the PO and flag only the exceptions
- Capture dock imagery as evidence attached to the discrepancy
Reads documents the supplier already sends; the ERP write is a single endpoint.
Yard and asset visibility without hand-counting
Nobody can say what is in the yard right now without someone walking it with a clipboard.
Assets are hired to replace assets already on site. Dwell time is estimated, never measured.
- Count and classify from fixed cameras and periodic drone passes
- Track dwell time per asset and flag anything overdue
- Expose a live count the yard team can actually trust
Outdoor mounting and power, in weather, before anything can be tested.
Grid and asset inspection footage nobody has time to watch
Drone and helicopter inspection footage accumulates faster than engineers can review it, so deterioration is caught late.
Faults are found on the next inspection cycle, or by an outage. Review capacity caps how often you can inspect.
- Batch-process captured footage against known condition classes
- Rank assets by severity so engineers review the worst first
- Retain evidence frames and location against every finding
Runs against footage you have already collected. No site visit in the loop.
Site progress and safety verified from what is actually there
Progress is self-reported by subcontractors and verified by a site walk, when someone has time to do one.
Claims are paid against progress that has not happened. Disputes are settled from photographs nobody organised.
- Derive progress from fixed cameras and periodic captures
- Verify claimed milestones against observed state
- Flag safety violations as they happen rather than at review
Site access is scheduled around the trades who are actually building.
Engineering knowledge locked in the heads of four people
Design decisions are spread across wikis, tickets and a decade of documents. Finding them depends on knowing who to ask.
New engineers take months to become useful. The same architectural question is re-answered every quarter.
- Connect the systems the answers already live in
- Respect existing document permissions in retrieval, not just at display
- Answer with a citation, or refuse
Existing repositories, cloud, no regulated data. Close to the ceiling.
Policy answers a supervisor can defend to a regulator
Regulatory and policy documents change constantly, and staff need answers that survive supervisory review.
Staff either guess or escalate. A confidently wrong answer from a generic assistant is a reportable event.
- Version-aware ingestion so answers reflect policy as it stands today
- Every response carries the clause it was drawn from
- Refusal is the default when no supporting clause exists
- Full query audit log, exportable for supervisory review
The retrieval is quick; the compliance sign-off is on the reviewer’s calendar.
Clinical documentation burden that is driving attrition
Clinicians spend a large share of every shift on documentation that is necessary, repetitive and hated.
Documentation happens after hours. Coding accuracy suffers, and so does retention.
- Draft structured documentation from the encounter record
- Surface coding suggestions with the evidence for each
- Clinician approves, nothing enters the record unreviewed
Clinical validation and a HIPAA review, both on cycles we do not control.
Contract obligations nobody is tracking after signature
Obligations, renewal dates and liability caps live in PDFs that were read once, at signing.
Auto-renewals are discovered after they renew. Obligations are breached because nobody knew they existed.
- Extract obligations, dates and caps into a tracked register
- Alert owners before a date matters, not after
- Answer "what are we committed to" with the clause attached
Contracts are already digital and the obligations are extraction, not integration.
Support volume that grows linearly with customers
Agents re-answer the same questions, and generic assistants produce confident answers that are wrong.
Headcount scales with growth. Deflection tools get switched off after the first bad answer reaches a customer.
- Index resolved tickets alongside current documentation
- Answer only where a supporting source exists
- Escalate to a human when confidence is low, with the context attached
- Expose deflection and quality metrics honestly
Ticket history exists, integrations are SaaS APIs, no compliance gate.
Claims and invoice processing done by people reading PDFs
Documents arrive in every format imaginable and are keyed into a system by hand.
Throughput is capped by headcount. Error rates rise at exactly the moment volume does.
- Extract structured data whatever the format it arrives in
- Route only genuine exceptions to a person
- Reconcile against the source system and hold an audit trail
Formats were known and the finance system had a usable API.
Shrink and checkout loss visible only in the monthly count
Loss is measured at stocktake, months after the behaviour that caused it.
You know the number and never the cause. Interventions are guesses.
- Detect the events that correlate with loss at the point they happen
- Attribute to store, lane and time rather than to a monthly total
- Feed the existing loss-prevention workflow, not a new one
Store hardware, but the estate is uniform so one install pattern repeats.
Legacy systems that block every project that touches them
The system of record cannot be replaced and will not integrate, so every initiative routes around it.
Each project pays the same integration tax. Data is re-keyed between systems by people.
- Wrap the legacy system in an API surface it never had
- Reconcile continuously so both sides can be trusted
- Migrate incrementally, with a rollback at every step
Change windows on the system of record are weekly, so cutover waits twice.
Not listed? Bring it anyway.
ChiefManager™ classifies from what you describe, not from a menu. If it resolves to vision or retrieval, it can be scoped and priced.