# AI Factories

> AI factories from Boltcliq: compute, data, tooling and operating model that turn ideas into deployed models and agents on a predictable schedule, for enterprises, governments and sovereign programmes.

Canonical URL: https://www.boltcliq.com/solutions/ai-factories
Part of: https://www.boltcliq.com/llms.txt

Repeatable production lines for building, testing and shipping intelligence.
- An AI factory is compute, data, tooling and process working as one line, so that ideas become deployed models and agents on a predictable schedule.
- Repeatable: The same path from idea to production every time
- Governed: Evaluation and approval built into the line
- Right-sized capacity: Compute sized to real workloads and budgets
- Operated by you: A team and practice that keep output steady
### What we do
- Many organizations ship one AI project a year, by heroic effort. The second one starts from scratch. An AI factory changes that. It is the combination of compute, data access, build and evaluation tooling, release controls and a team, so that the tenth model or agent takes far less effort than the first and meets the same standards.
- We design and stand up the factory around your goals. For a bank it may be a line of credit and fraud models and service agents. For a ministry it may be a shared capability that agencies use under common rules. For a sovereign programme it may be national compute with a catalogue of models and a way to share them safely. We start with the work the factory must produce, then size the infrastructure and the operating model to match.
- The hard parts are rarely the GPUs. They are data access that is both fast and governed, evaluation that people trust, a release process that does not need a committee every time and a team that knows who owns what. We build those with you, and we build in the controls that production demands: approvals, audit, cost tracking and rollback.
### A line, not a project
- A factory has stages. Ideas come in and are triaged. Data is prepared and checked. Models and agents are built, then tested against agreed measures. Approved releases are deployed, monitored and improved. Each stage has an owner, a standard and a record.
- That structure lets a small team handle a growing number of products without losing quality. It also lets leaders see what is in the pipeline, what it costs and what it has delivered.
- Intake, build, evaluate, release and operate
- An owner and a standard at each stage
- A portfolio view of work, cost and results
### Capabilities
**Compute and infrastructure**
- We plan capacity for training, fine-tuning and inference based on your real workloads, not on peak benchmarks. Options include public cloud, private clusters and in-country partner capacity. We compare cost, availability, latency and residency, and design for scheduling and sharing so expensive hardware does not sit idle.
- Capacity plans from workload analysis
- Cloud, private and partner options compared
- Scheduling and quota management
- Idle accelerators are the most expensive kind.
**Data and feature pipelines**
- We build the pipelines that deliver governed data to builders: access requests, de-identification where needed, versioned datasets and feature stores. Lineage shows which data trained which model. Sensitive data stays in controlled zones.
- Versioned datasets and features
- Access requests and de-identification
- Lineage from data to model
- Fast, governed data access is the true bottleneck.
**Model and agent pipelines**
- We set up build, test and release pipelines for models and agents: experiment tracking, reproducible builds, automatic evaluation, staged rollout and rollback. Agents are tested with scenarios and adversarial inputs before they reach users. Each release carries its test results.
- Experiment tracking and reproducible builds
- Automated evaluation suites
- Staged rollout and rollback
- Speed without tests is a faster way to fail.
**Evaluation and safety**
- We build evaluation sets with your domain experts and run them on every release. Tests cover accuracy, robustness, bias, privacy leakage, cost and latency. Results feed an approval gate that names who can release and under what conditions.
- Domain-expert evaluation sets
- Bias, robustness and privacy tests
- Approval gates with named approvers
- Trust in the output comes from trust in the tests.
**Factory operations**
- We define the roles, rituals and metrics that keep output steady: intake and prioritisation, on-call, incident review, cost review and a regular retirement of unused models. Dashboards show throughput, quality, cost per release and utilisation.
- Roles, rituals and service levels
- Throughput, quality and cost dashboards
- Retirement of unused assets
- Steady output is an operating discipline.
**Sovereign and shared factories**
- For governments and consortia we design shared factories where several agencies or companies use common compute and tooling under separate policies. We design tenancy, cost sharing, model catalogues and rules for data use. Legal and policy design is led by your counsel and policy teams.
- Multi-tenant compute and tooling
- Model catalogue and sharing rules
- Cost allocation across members
- Shared capacity lowers the cost of entry.
### Where it applies
- Banks and insurers
- A group wants dozens of models and agents for credit, fraud, claims and service but has no repeatable way to ship them under model risk rules.
- We stand up a factory with evaluation gates, documentation and audit that matches model risk practice, so each release carries its evidence.
- The model risk committee sets standards and approves high-impact releases.
- Government and sovereign programmes
- A national programme wants shared AI capability for agencies, without each agency building its own stack.
- A shared factory offers compute, data access under agreement, tooling and a catalogue of approved models, with per-agency policy.
- Programme leadership and legal teams decide on data sharing and use cases.
- Telecommunications
- An operator runs many models for churn, fraud and network planning in different teams, with duplicated effort.
- A common factory provides shared data, features, evaluation and deployment, with cost tracking per team.
- Business owners decide which models to fund and retire.
- Healthcare networks
- A hospital group wants to deploy imaging and operations models across sites with strict privacy needs.
- A factory with de-identification, local evaluation and controlled deployment lets each site validate models before use.
- Clinical governance boards approve models for clinical use.
### Built on
**Argus**
- Provides environments, storage, sandboxes and secrets for builders and pipelines, portable across providers.
- Boltcliq product
- A ready runtime for the line.
**Monolith**
- Governs agent context, memory and permissions, and holds the rules that releases must meet.
- Boltcliq product
- Governance is part of the pipeline.
**Cyclopes**
- Tracks quality, cost, utilisation and incidents across every release.
- Boltcliq product
- Leaders see what the factory delivers.
### What we do and do not do
- Design the factory, its stages, roles and standards
- Plan and set up infrastructure with you
- Build pipelines for data, models and agents
- Set up evaluation and approval gates
- Train and support the factory team
- Sell hardware or promise specific chips
- Release a model without evaluation
- Build a factory before there is work for it
- Own your models or data
- Operate the factory for you indefinitely
### Questions we hear
- Do we need our own GPUs?
- Not always. We compare cloud, partner and owned capacity against your real workloads. Many organizations start with rented capacity and move parts of the workload as demand becomes steady.
- How big does a factory need to be?
- It should match the work you can feed it. We size from workloads and budget, and design the line so it can grow in stages.
- Can the factory run in our country?
- Yes, with in-country facilities or partners and a portable platform. Which options are available varies by country. Confirm data location rules with counsel.
- How do you handle model risk and approval?
- Each release carries evaluation results and goes through named approvers. We align the gates with your model risk or clinical governance process.
- What does the team look like?
- Typically a platform team, data engineers, evaluation specialists, product owners and operations. We help you decide the minimum team for your scale.