Throughput Labs exists because of a gap we kept running into: strategy firms that produce excellent analysis and never touch the systems, and software shops that build competently without understanding the operation they are building for. Throughput problems require both, held by the same people.
We are a senior team of operations engineers, data engineers and applied AI practitioners. Our consultants have run manufacturing schedules, closed month-end, managed warehouse floors and carried production pagers. That background is deliberate — it is what allows someone to stand in a plant for three days and identify which queue actually governs the output.
We also write the code. The same people who conduct the discovery build the system, which removes the handoff where most transformation programmes lose their fidelity. There is no separate delivery organization reinterpreting a report they did not author.
We work with organizations from roughly 50 to 5,000 employees, in any sector where information technology carries the work. We operate through satellite teams across Europe and North America, which means an on-site mandate is staffed from a team already in your region and your time zone rather than flown in for the week. We work in English and French.
A process has a measurable capacity, a bottleneck, and a queue. Those facts do not change because a steering committee prefers a different narrative. Our first act in any engagement is to make them visible and quantified, which is occasionally uncomfortable and always necessary.
A language model is a remarkably good reader of unstructured input and a remarkably poor system of record. Used as one component inside a properly engineered workflow, it is transformative. Used as the workflow itself, it produces an impressive demonstration and an operational liability.
In eight out of ten engagements, the operator on the floor named the real constraint in the first conversation. Our value is not discovering it for the first time — it is measuring it, proving it to the organization, and engineering the fix. That is also why we work on-site.
If you cannot reconstruct why the system posted that invoice or approved that request eight months later, accuracy is irrelevant. Traceability is designed in from the first commit, not retrofitted when the auditor asks.
A technically correct system that operators route around has zero throughput value. We design the human touchpoints — the review queue, the override, the escalation path — with the same rigour as the inference pipeline, because that is where adoption is won or lost.
A meaningful fraction of the constraints we find are resolved by a database index, a corrected approval threshold or a removed redundant sign-off. We say so, in writing, even when a larger engagement was available. It is the only sustainable way to run this kind of practice.
We are tool-agnostic by policy and select per constraint. The following reflects what we run in client production environments today.
Python, TypeScript, SQL, Go. FastAPI, Node, Celery. Polars and pandas for analysis, DuckDB for local exploration.
Temporal for durable workflows, n8n for lighter integration paths, Airflow and Dagster for batch, native ERP job schedulers where appropriate.
PostgreSQL with pgvector, Snowflake, BigQuery, SQL Server. dbt for modelling, Debezium for change-data capture, Kafka and SQS for messaging.
Azure OpenAI, AWS Bedrock, Anthropic and Google APIs. Self-hosted open-weight models on vLLM or Ollama where data residency requires it.
Hybrid BM25 plus dense retrieval, cross-encoder reranking, OpenSearch and Elasticsearch, Qdrant, structured metadata filtering and entitlement-aware indexes.
OR-Tools and CP-SAT for scheduling and routing, linear and mixed-integer programming, discrete-event simulation for capacity questions.
Docker, Kubernetes, Terraform. Azure, AWS and GCP, plus on-premise and air-gapped deployments on client hardware.
OpenTelemetry, Prometheus and Grafana, structured logging, model-level tracing with cost and confidence telemetry per transaction.
Engagements succeed or fail on access — to people, to systems, and to the truth about how things currently run. Before we start, we agree on five things.
We would rather tell you now than three months in.
A 30-minute call, no material required. Describe where work piles up and we will tell you what measuring it would involve.
