Solutions
From raw data to defensible answers
Five solution lines, delivered by one team. Engagements usually start with a sample of your data and a clear definition of done.
Data Enrichment & Classification
Most enterprise data is locked in scans, recordings, chat exports, and mixed-language files. We extract it, label it, and return it as structured fields your teams can search, report on, and act on.
What we deliver
- OCR for scanned and image-based documents, with language detection and routing
- Machine translation with source and target text kept side by side
- Speech transcription, speaker separation, and captions
- Entity extraction, topic and category labels, and sensitivity and privilege flags
- Image and video analysis: object detection, keyframes, and plate and text reads
- News and content intelligence: summaries, bias and framing scores, story clustering
Typical engagements
- Backlog OCR and translation for a matter or archive
- Classification of a file share or mailbox ahead of a migration
- Ongoing enrichment feeds for a monitoring or research team
eDiscovery & Forensics Readiness
We help legal and investigations teams get data review-ready, and keep a defensible record of everything that touched it.
What we deliver
- Processing and text extraction for review platforms
- Chat and mobile data conversion to RSMF, with validation
- Load-file QC and repair (DAT, OPT, LFP, and related formats)
- Forensic analysis: hashing, known-file matching, device and EXIF metadata
- Image authenticity and AI-generated media checks
- Redaction support and legal-hold workflows that leave originals untouched
Typical engagements
- Readiness assessment of a collection before it goes to review
- Conversion of chat and collaboration data for production
- Load-file remediation after a failed import
Data Platforms & Migration
Migrations fail on the details: formats, metadata, and lineage. We plan and run them so the data arrives complete, enriched, and traceable to its source.
What we deliver
- Platform-to-platform and format migrations with metadata preserved
- Data lake and vector index design for enriched content
- BI-ready exports (CSV, Parquet, and load files) with lineage back to every source file
- Near-duplicate detection and document similarity to reduce volume
- Retention, classification, and hold-aware cleanup for governance programs
Typical engagements
- Review-platform or archive migration
- Building an enriched, searchable data lake from legacy repositories
- Governance cleanup ahead of a retention policy rollout
AI Engineering & Custom Models
General-purpose models stall on specialized data. We build and adapt models for a specific corpus, and measure them on held-out data before they are trusted with real work.
What we deliver
- Custom model development, fine-tuning, and distillation
- Retrieval and search systems (RAG) with embedding evaluation and drift checks
- AI agents and tool integrations (including MCP) with human approval steps
- Evaluation harnesses and reporting that separate tested results from hopes
- Architecture and engineering for production AI services: APIs, queues, telemetry
Typical engagements
- A custom classifier or extraction model for a document type
- Search and question answering over a large internal corpus
- An agent workflow with approval gates for a repetitive process
AI Infrastructure (Neocloud)
Some workloads cannot go to a public AI API. We run models and enrichment pipelines on GPU infrastructure we own and operate, or deploy into your environment.
What we deliver
- Dedicated GPU capacity for enrichment, fine-tuning, and batch inference
- Private model hosting and inference endpoints
- Managed AI infrastructure: Kubernetes packaging, scheduling, and monitoring
- On-premises and isolated-network deployments of our pipelines
Typical engagements
- Private inference for sensitive documents
- Batch enrichment of a large collection on dedicated hardware
- Deploying a pipeline into a customer data center
Not sure which line fits?
Most engagements cross two or three of these. Send us a short description of the data and the deadline and we will propose a starting point.