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July 27, 2026
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EdgeForge
Model Compression

One pipeline to compress heavy models for edge deployment —
and one benchmark to prove the accuracy survived the trip.

Project Details

Getting a large model onto an edge device means trading accuracy against latency and size, and most teams make that trade blind — across siloed toolchains, with no consistent way to measure what each pass actually cost them. EdgeForge unifies it.

Quantisation, pruning and distillation run as reproducible job graphs, so a build is deterministic and repeatable. ONNX conversion is followed by TensorRT or OpenVINO compilation passes, targeted per runtime. Every pass is benchmarked for latency, model size and accuracy retention against scenario-specific targets, with original-versus-compressed diffs shown side by side and validation-driven accuracy retention graphs that catch silent regressions before they ship. Deployment exports are packaged as versioned artefact bundles with full build metadata, so a rollout can always be traced and rolled back.

DELIVERABLES
Compression pipeline Runtime optimisation Benchmarking platform
INDUSTRY
Edge AI MLOps

Project Research

The central design question was how to prevent silent accuracy loss — the failure mode where a compressed model looks fine on aggregate metrics but has quietly collapsed on a critical class.

The answer was to make evaluation a mandatory, gated stage of the pipeline rather than an optional afterthought: no artefact is exported without a validation run against the target scenario, and the accuracy retention graph is the artefact everyone looks at first.

Project Results

EdgeForge delivers typical size reductions of 6× with 90% accuracy retention and sub-50ms P50 latency on target edge hardware. Export is a single click, producing a reproducible config and a versioned build bundle. What used to be a fortnight of manual toolchain wrangling per model is now a pipeline run.

FlowForge
Agentic Automation
View Project

FlowForge
Agentic Automation

DESCRIPTION
Replaced brittle RPA scripts with self-healing AI agents that route, approve and execute across the existing stack — 70% of manual ops removed at a 99.3% run success rate.
DELIVERABLES
Agent runtime, Tool orchestration, Human-in-the-loop, Full tracing
INDUSTRY
Enterprise Operations