# Cybergizer

> Cybergizer is an AI implementation and software engineering company. We help product and operations teams connect AI to existing applications, data and day-to-day work.

Services: AI strategy and discovery, AI systems and product development, AI adoption for engineering teams and software modernization. We start with one business problem (a manual workflow, an AI product feature or a software delivery bottleneck), define the scope, build the integrations and agree how your team will accept and operate the result. Agents help do the work; people own the decisions. Cybergizer's published work includes Ruby on Rails, React, PostgreSQL and Python, along with local and API-based model tooling. Communities: Baltic Ruby, Ruby Europe, AI Foundry. Contact: sales@cybergizer.eu, enquiry form at https://cybergizer.com/contact/. Website language: English.

## Main pages

- [Home](https://cybergizer.com/): AI strategy, systems development, engineering enablement and software modernization. Start with one workflow or product goal.
- [How we deliver AI projects](https://cybergizer.com/approach/): From shared context and acceptance criteria to implementation, evaluation, release and handover with an accountable engineering lead.
- [AI assisted engineering delivery system](https://cybergizer.com/delivery-system/): How project context, agents, tests and human decisions fit into Cybergizer delivery.
- [Why work with Cybergizer](https://cybergizer.com/why-cybergizer/): Understanding business work, defining technical scope and maintaining engineering accountability through implementation.
- [Engineering capabilities](https://cybergizer.com/engineering/): Application engineering, AI integration, data workflows and deployment capabilities.
- [AI maturity and readiness assessment](https://cybergizer.com/ai-maturity/): AI readiness across workflow, engineering, data, governance and adoption, with maturity stages.

## Services

- [AI services overview](https://cybergizer.com/services/): Compare AI discovery, development, engineering enablement and modernization services.
- [AI strategy & discovery](https://cybergizer.com/services/ai-strategy/): Assess AI opportunities against your workflow, data and operating constraints; build-versus-buy options, evaluation criteria and a practical first scope.
- [AI systems & product development](https://cybergizer.com/services/ai-development/): Build AI into products and business processes with data integrations, evaluation, human review and a path to supported operation.
- [AI adoption for engineering teams](https://cybergizer.com/services/ai-enablement/): Improve how your team uses AI across requirements, development, review and delivery.
- [Software modernization](https://cybergizer.com/services/modernization/): Modernize existing software around a defined business constraint, improve test coverage and release through an agreed acceptance path.
- [Enterprise AI implementation](https://cybergizer.com/services/enterprise-ai/): Connect an enterprise AI program to data, permissions, existing systems and accountable delivery.
- [Focused AI engineering pilot](https://cybergizer.com/services/engineering-pilot/): One team, a defined workflow and testable acceptance criteria before a broader AI implementation.

## Solutions

- [AI document workflow automation](https://cybergizer.com/solutions/document-workflow-automation/): Connect document intake, AI extraction and human review to your existing systems.
- [AI customer integrations for B2B AI vendors](https://cybergizer.com/solutions/ai-customer-integrations/): Customer data, APIs, identity, evaluation and handover for one blocked customer integration.
- [AI implementation for business workflows](https://cybergizer.com/business-ai/): Turn a repeated business task into an integrated AI workflow.
- [AI features for existing products](https://cybergizer.com/product-ai/): Plan and integrate an AI feature around a real user need.
- [Customer deployment and FDE partnerships](https://cybergizer.com/deployment-partners/): Deployment engineering for B2B AI vendors alongside their product and solutions teams.
- [AI for investment and portfolio operations](https://cybergizer.com/private-equity/): Assess AI opportunities at the process level across portfolio operations.

## Industries

- [AI implementation by industry](https://cybergizer.com/industries/): Overview of industry pages.
- [Healthcare & life sciences](https://cybergizer.com/industries/healthcare/): Administrative and operational workflows with human review, data permissions and clinical boundaries.
- [Insurance](https://cybergizer.com/industries/insurance/): Underwriting, claims and policy operations with traceable data, evidence and defined human decisions.
- [Manufacturing & industrials](https://cybergizer.com/industries/manufacturing/): Engineering knowledge, document access and exception handling with a defined operational owner.
- [Consumer goods & retail](https://cybergizer.com/industries/consumer-goods/): Product information, content operations and customer workflows with source data and review controls.
- [Digital products & growth teams](https://cybergizer.com/industries/digital-natives/): AI features and delivery workflows with evaluation, integrations and operating cost in view.

## Project experience

- [AI and software project experience](https://cybergizer.com/work/): Overview of Cybergizer project contributions.
- [Respondology](https://cybergizer.com/work/respondology/): NLP, machine learning and automated comment moderation with Ruby, PostgreSQL and AWS.
- [Talentako](https://cybergizer.com/work/talentako/): Candidate CV anonymization, sensitive-data detection and a review interface for recruitment workflows.
- [Waterman](https://cybergizer.com/work/waterman/): AI demonstration for UK legislation analysis, document extraction and configurable analysis workflows.
- [Llamagator](https://cybergizer.com/work/llamagator/): Published multi-model prompt testing tool comparing responses across local and hosted models.

## Tools

- [AI model and provider comparison](https://cybergizer.com/model-intelligence/): Compare hosted AI models and providers by published prices, context and capabilities; estimate token costs.
- [AI workflow value case calculator](https://cybergizer.com/tools/value-case/): Model an AI workflow value case using explicit volume, effort, review and operating-cost assumptions.

## Blog

- [Blog](https://cybergizer.com/blog/): Articles, experiments and working notes from the Cybergizer team.
- [Two years of forward-deployed engineers: what we learned by starting with ourselves](https://cybergizer.com/blog/two-years-of-forward-deployed-engineers/): Retraining our engineers as forward-deployed engineers, starting with our own team and product.
- [Private AI: How to Use LLMs Without Sending Your Data to OpenAI](https://cybergizer.com/blog/private-ai-how-to-use-llms-without-sending-your-data-to-openai/): What happens to data sent to public AI tools, three ways to run private AI, their costs and how to decide.
- [Briefing a coding agent on an existing repository](https://cybergizer.com/blog/briefing-a-coding-agent-on-an-existing-repository/): The task, the repository rules and a short review loop before an agent touches a client codebase.
- [Why a blog next to Insights](https://cybergizer.com/blog/why-a-blog-next-to-insights/): What goes in the blog and what stays in Insights.

## Insights

- [AI engineering and implementation insights](https://cybergizer.com/insights/): Practical perspectives on AI workflow design, evaluation, governance, adoption and modernization.
- [Where does the work go after the AI output?](https://cybergizer.com/insights/ai-workflow/): A generated answer is only part of a business process.
- [What an engineering team needs around its AI tools](https://cybergizer.com/insights/ai-engineering/): Access to a coding assistant is the beginning of adoption.
- [Choose the smallest modernization that removes the constraint](https://cybergizer.com/insights/modernization/): A useful next step can be smaller than a rewrite.
- [Ask an AI delivery partner for an operational plan](https://cybergizer.com/insights/ask-an-ai-delivery-partner-for-an-operational-plan/): A consulting engagement should leave a working increment, explicit decisions and a team that can own the result.
- [Assess portfolio AI opportunities at the process level](https://cybergizer.com/insights/assess-portfolio-ai-opportunities-at-the-process-level/): Identify repeatable implementation conditions across different portfolio businesses.
- [Assess readiness for one workflow you could actually deliver](https://cybergizer.com/insights/assess-readiness-for-one-workflow-you-could-actually-deliver/): Readiness as specific prerequisites, people and evidence for a bounded task.
- [Assign AI ownership across the delivery lifecycle](https://cybergizer.com/insights/assign-ai-ownership-across-the-delivery-lifecycle/): Who selects work, approves controls, pays for operation and responds when the system fails.
- [Build an AI roadmap from real deployment dependencies](https://cybergizer.com/insights/build-an-ai-roadmap-from-real-deployment-dependencies/): Sequence decisions and prerequisites around a small number of workflows.
- [Build an operational control pack for healthcare AI](https://cybergizer.com/insights/build-an-operational-control-pack-for-healthcare-ai/): Describe the system, its responsibilities and exception handling before evaluating a deployment.
- [Buy AI implementation with an explicit acceptance path](https://cybergizer.com/insights/buy-ai-implementation-with-an-explicit-acceptance-path/): A procurement brief linking workflow, access, evaluation, responsibilities and commercial conditions.
- [Choose an AI supplier by testing the work](https://cybergizer.com/insights/choose-an-ai-supplier-by-testing-the-work/): A supplier decision should survive real inputs, integration constraints and a clear handover.
- [Choose automation by the kind of uncertainty in the work](https://cybergizer.com/insights/choose-automation-by-the-kind-of-uncertainty-in-the-work/): Where deterministic automation is enough and where AI assistance needs additional controls.
- [Convert an AI ambition into a paid delivery decision](https://cybergizer.com/insights/convert-an-ai-ambition-into-a-paid-delivery-decision/): Start with a specific task and earn the next commitment through evidence.
- [Decide how much authority your AI workflow needs](https://cybergizer.com/insights/decide-how-much-authority-your-ai-workflow-needs/): Choose assistance or delegated action by the decision, its consequences and maintainable controls.
- [Design AI as a component of the enterprise system](https://cybergizer.com/insights/design-ai-as-a-component-of-the-enterprise-system/): Connect identity, data contracts, task execution and operation, not just a model endpoint.
- [Design claims support around reviewable case files](https://cybergizer.com/insights/design-claims-support-around-reviewable-case-files/): Evidence preparation and exception handling with accountable staff keeping decisions.
- [Design human review as part of the product](https://cybergizer.com/insights/design-human-review-as-part-of-the-product/): Human review needs usable evidence, clear authority and enough time.
- [Diagnose adoption friction in the actual process](https://cybergizer.com/insights/diagnose-adoption-friction-in-the-actual-process/): Investigate the task, incentives and handoffs before adding training.
- [Find the waiting time your AI tools leave behind](https://cybergizer.com/insights/find-the-waiting-time-your-ai-tools-leave-behind/): Faster tasks matter only when the whole process reaches an accepted result.
- [Give agent workflows a run boundary](https://cybergizer.com/insights/give-agent-workflows-a-run-boundary/): A defined purpose, a controlled action space and an observable stopping condition for each run.
- [Govern agent actions through permissions and evidence](https://cybergizer.com/insights/govern-agent-actions-through-permissions-and-evidence/): Control specific actions, record their context and give people a route to intervene.
- [Implement clinical operations support with clear handoffs](https://cybergizer.com/insights/implement-clinical-operations-support-with-clear-handoffs/): Integrate with staff responsibilities, source records and existing procedures.
- [Improve software delivery without rewarding code volume](https://cybergizer.com/insights/improve-software-delivery-without-rewarding-code-volume/): Evaluate AI assisted delivery by acceptance, meaningful review and operational consequences.
- [Inspect AI claims before committing engineering resources](https://cybergizer.com/insights/inspect-ai-claims-before-committing-engineering-resources/): Technical diligence on dependencies, operating costs and evidence gaps.
- [Keep delivery accountability when AI speeds up coding](https://cybergizer.com/insights/keep-delivery-accountability-when-ai-speeds-up-coding/): Delivery ownership must stay explicit from requirements through release and operation.
- [Make AI policy executable in delivery decisions](https://cybergizer.com/insights/make-ai-policy-executable-in-delivery-decisions/): Identify which controls apply to a workflow and produce evidence that they operate.
- [Make an AI purchase decision at the workflow level](https://cybergizer.com/insights/make-an-ai-purchase-decision-at-the-workflow-level/): Compare build and buy against the same operational scope, including ownership after implementation.
- [Make underwriting support traceable before automating steps](https://cybergizer.com/insights/make-underwriting-support-traceable-before-automating-steps/): Explicit sources, review boundaries and exception ownership in underwriting work.
- [Manage model selection as an ongoing operational decision](https://cybergizer.com/insights/manage-model-selection-as-an-ongoing-operational-decision/): A model choice needs a review process after the first release.
- [Measure accepted work instead of AI activity](https://cybergizer.com/insights/measure-accepted-work-instead-of-ai-activity/): Evaluate a deployment through completed work, total effort and operating cost.
- [Modernise existing systems around one useful workflow](https://cybergizer.com/insights/modernise-existing-systems-around-one-useful-workflow/): Address the data and integration constraints that block a useful task first.
- [Modernise the workflow before expanding the AI estate](https://cybergizer.com/insights/modernise-the-workflow-before-expanding-the-ai-estate/): Connect modernisation to a specific operational constraint.
- [Move a useful pilot into a supported deployment](https://cybergizer.com/insights/move-a-useful-pilot-into-a-supported-deployment/): Close operational gaps and confirm the receiving team can own the workflow.
- [Operate the AI workflow as a service](https://cybergizer.com/insights/operate-the-ai-workflow-as-a-service/): Production readiness means a team can understand, support and change the workflow.
- [Prepare evidence before deploying AI in controlled environments](https://cybergizer.com/insights/prepare-evidence-before-deploying-ai-in-controlled-environments/): Clear boundaries, reviewable evidence and accountable approvals.
- [Prioritise AI work by evidence and executability](https://cybergizer.com/insights/prioritise-ai-work-by-evidence-and-executability/): Use cases with a real process problem, accessible data, an accountable buyer and a path to operation.
- [Reduce deployment exposure one decision at a time](https://cybergizer.com/insights/reduce-deployment-exposure-one-decision-at-a-time/): Limit initial scope and make failures detectable, recoverable and owned.
- [Select healthcare AI projects around administrative work](https://cybergizer.com/insights/select-healthcare-ai-projects-around-administrative-work/): Choose a bounded administrative workflow that can be tested before wider deployment.
- [Structure agent workflows around recoverable steps](https://cybergizer.com/insights/structure-agent-workflows-around-recoverable-steps/): Separate interpretation from consequential actions, with a contract, status and recovery path per step.
- [Turn an AI business case into a delivery ledger](https://cybergizer.com/insights/turn-an-ai-business-case-into-a-delivery-ledger/): Link the proposed benefit to a workflow change, an owner and collectable evidence.
- [Use AI native to describe a design choice](https://cybergizer.com/insights/use-ai-native-to-describe-a-design-choice/): An AI native claim should explain how the workflow and operating model handle uncertain output.

## Contact

- [Discuss your AI implementation](https://cybergizer.com/contact/): Describe your workflow, product or deployment challenge; enquiries also go to sales@cybergizer.eu.
- [LinkedIn](https://www.linkedin.com/company/cybergizer/): Company page.
- [GitHub](https://github.com/cybergizer-hq): Cybergizer on GitHub.
- [Events](https://lu.ma/cybergizer): Cybergizer events on Luma.

## Optional

- [Example AI implementation briefs](https://cybergizer.com/sample/): Example briefs turning a workflow question into users, constraints, integration scope and acceptance criteria.
- [Engineering collaboration](https://cybergizer.com/for-engineers/): How engineers collaborate with Cybergizer alongside client teams.
- [Website privacy note](https://cybergizer.com/privacy/): Website enquiries, optional Google Analytics, local tools and analytics choices.
- [Sitemap](https://cybergizer.com/sitemap.xml): Full list of indexed pages.