Category 04 / Back-Office Support
Not generic BPO. Not data-entry sweatshops. This is the structured work that feeds annotation queues, normalises raw input, and keeps live AI pipelines fed with clean, schema-conformant data — by trained operators, not general-purpose staff.
Operating model
Use cases
Real scenarios we run today. Each one sits inside someone else’s AI stack — the invisible but critical layer that decides whether the rest of the pipeline is worth running.
Converting raw scans, PDFs, or messy CSVs into structured records ready for labelling. Normalised fields, consistent identifiers, deduped at source — so the annotation team isn’t paid to clean data.
OCR cleanup, field validation, metadata tagging for archive corpora. Regulated paper input — forms, contracts, ledgers — turned into training-ready structured records inside a secured environment.
Continuous structured input into your production AI system — the human fallback layer. We handle the stream that doesn’t stop: inbound documents, exception items, content entering the model in real time.
Restructuring legacy databases into modern training-ready formats. Field mapping, type coercion, reconciliation across sources. Documented, reversible, versioned — not a one-shot script.
Adding missing fields, normalising formats, deduplication at scale. Entity resolution, attribute completion, canonicalisation against an authoritative reference — the boring work that makes the model’s work possible.
The judgment-call layer that catches what RPA and classifiers miss. Low-confidence items escalated to trained operators, resolved, and folded back into the pipeline with a written decision record for retraining.
How we deliver
Four stages on every engagement. The edge cases get surfaced in Intake; nothing reaches Delivery that hasn’t been through Calibration.
We read your source data as it really is — formats, volumes, quality, edge cases. Dirty files, not idealised samples. The intake profile becomes the working document for the rest of the engagement.
We co-write the target output structure with your data lead. Types, cardinalities, required fields, nullability, the edge-case rules — documented and versioned before any operator touches the data.
Operators run a calibration batch on your sample data. QA thresholds set against that batch, not a generic baseline. Anyone not hitting the floor doesn’t get production access — no exceptions.
Continuous delivery against written SLAs. Weekly quality reporting, escalation paths defined on day one, rework within SLA if we miss the floor. The pipeline stays fed — that’s the only metric that matters.
Non-negotiables
Back-office work earns its premium by being measurable. Every record is attributable; every batch has a QA footprint; every exception has a written resolution.
The quality footprint is built in, not bolted on. No batch ships without a QA pass; no output ships without per-operator attribution.
The certification isn’t a logo. It maps to actual operating controls — access, premises, data residency, exception handling — that get audited on their own cadence.
What we’ve delivered
Structured records produced against client schemas across back-office engagements to date.
Written threshold on structured-entry engagements. Sample-based QA on every batch; rework within SLA.
Batch turnaround window on steady-state engagements. Throughput scales with the pipeline, not the backlog.
PDF, scan, XLSX, CSV, JSON, image, and client-custom formats — handed in, handed out to your schema.
Scope with us
We look at your input data, your target schema, your volume. Then we propose a team, an SLA, a price. No cold quotes, no volume-only pricing — the scope decides the number.