Where AI/ML becomes
a business process.
Twenty-six pipelines in three groups. Eight ML workflows, nine agentic workflows and nine industry blueprints.
Measure before you train.
Compare before you ship.
Before fine-tuning, the pipeline asks the base model the held-out questions. If it already knows the answers, training would prove nothing, so a gate stops the run. Here the base model knew almost none of them, the gate passed, and training started inside a $50 budget.
Qwen2.5-1.5B-Instruct on 75 held-out facts, measured before training. The gate lets training start only below 10% correct. After training, the same questions run again and a person sees both results side by side. See the pipeline →
ML workflows
Train, align, evaluate, compress and run models you own, with the checks built in.
Fine-tune and evaluate an LLM
Train on your GPUs, score every checkpoint, promote with approval.
Bring a dataset and a base model
Get an approved adapter with its scores
Evaluate and compare models
Score against your suites; flag regressions against a baseline.
Bring a model and a baseline
Get a gated verdict: gained, lost, or regressed
Prepare a dataset
Clean, deduplicate, decontaminate, redact, register.
Bring a file or bucket
Get a hash-pinned dataset version with findings
Compress a model
Distill or quantize; accept only if quality holds.
Bring a model that costs too much
Get a smaller model with its measured quality delta
Batch inference at a known cost
Price it first, refuse it over budget, record the real spend.
Bring a dataset and a prompt
Get a prediction dataset and its actual cost
Align with preferences (DPO)
Mine chosen/rejected pairs from graded answers; align; prove it.
Bring a trained adapter and graded answers
Get an aligned adapter that abstains instead of hallucinating
Learn from model failures
Grade, mine, retrain, compare; at most six cycles, then a person.
Bring a model that fell short
Get a better candidate, or a stop with the evidence
RL with verifiable rewards (GRPO)
Reward functions from your own checks; on-policy rollouts.
Bring a model and a verifiable check
Get a reasoning-tuned adapter
Pipelines marked with a name ship with Cairn and run on your infrastructure; backend support varies by deployment.
Agentic workflows
A model investigates, proposes and drafts. A person owns every conclusion.
An agent reasons, calls tools and iterates within one leg, inside a budget.
The pipeline between legs is declared. No agent decides what runs next.
Output waits for a person before it is accepted, written or shipped.
Every agentic leg: a budget per step · redaction on the way to the model · every tool call recorded.
Incident root cause and post-mortem
An agent correlates logs, traces and metrics; a person owns the cause.
Bring an incident window
Get an accepted root cause and a drafted post-mortem
Find the deploy that broke it
Tie an error onset to the most likely deploy, deterministically.
Bring an error onset and a deploy log
Get the most likely deploy, with the math
Check and repair a dataset
An agent narrates the verdict; a bounded loop repairs; approval before writes.
Bring a dataset that may not be ready
Get a passing dataset, or a stop with findings
Price a run against the GPU market
Live rentable offers, cheapest first, beside Cairn's own estimate.
Bring a planned run
Get what it would cost, where, before you commit
Compress with a quality budget
Baseline, quantize, re-evaluate, gate the delta.
Bring a shipped model and a regression budget
Get a compressed candidate, or a block
AI red-teaming campaigns
Generate attacks, escalate with approval, accept the findings.
Bring a target model
Get accepted findings and a report
CI failure triage
Cluster failing runs to their shared causes; propose the fix; a person files it.
Bring a day of failing CI runs
Get a short list of root causes with proposed fixes
Code review with gates
Findings with evidence; a reviewer accepts before anything is posted.
Bring a pull request
Get reviewed findings, posted only after acceptance
Cloud cost rightsizing
Find idle and oversized resources; propose changes; a person approves each.
Bring cloud usage and cost data
Get approved rightsizing changes with the evidence
Pipelines marked with a name ship with Cairn. Security alert triage is covered on the incident page.
Industry blueprints
Reference designs for data-, model- and compute-heavy problems. Start from one; a partner brings the domain model, Cairn brings the governed process.
Demand forecasting
Retail, CPG, distribution. Retrain on a check, not a calendar.
Bring sales history and a forecasting model
Get approved forecasts with error and stability evidence
Predictive maintenance
Manufacturing, mining, fleets. Retrain on verified outcomes.
Bring sensor history and verified maintenance outcomes
Get a model with a measured false-alert rate, approved before go-live
Industrial operations
Process and yield. Recommend setpoints; never control equipment.
Bring process parameters and yield or quality outcomes
Get approved setpoint recommendations with evidence
Visual inspection
Manufacturing, packaging, electronics. New defects, fresh labels.
Bring camera images and labelled defects
Get a vision model approved per defect class
Catalog enrichment
Marketplaces, retail. Extract attributes for millions of SKUs at a known cost.
Bring product titles, descriptions and images
Get a versioned, scored attribute dataset at a known cost
Media localization
Streaming, publishers. Transcribe, translate, dub, tag; QC before release.
Bring a back catalogue and target languages
Get localised, tagged assets that passed QC
Geospatial and remote sensing
Agriculture, forestry, mining. Fine-tune with scarce labels.
Bring imagery tiles and a few labelled regions
Get a fine-tuned segmentation model with proof of gain
Medical imaging
Radiology, pathology. De-identify, train, compare per finding, radiologist approves.
Bring de-identified studies with reads
Get a model compared per finding class, approved by radiologists
Medical billing and coding
Revenue cycle. Code encounters at scale; coders review before submission.
Bring de-identified clinical notes and coded history
Get proposed codes per encounter, reviewed before submission
Reference designs, not shipped packs. Each needs a partner with the domain model and data access. Also a fit, by request: delivery ETA and routing (shares the forecasting operator) and recommendation and search ranking.
Run it on your own infrastructure.
Apache 2.0 licensed, self-hosted. Start from any pipeline above, or tell us about the one you have.