Skip to content

For engineering teams

The same engineering Vectis Lab builds its product with, available to your team.

Vectis Lab's main work is its product: AI agents for back-office work that has to be right. The engineering behind it is also available to software teams, as eight services in three areas.

  • LLM systems
  • AWS
  • Cloud-native
  • Cloud infrastructure
  • Infrastructure as code
  • DevOps
  • DevSecOps
  • FinOps

Area 1 of 3

AI

AI systems development

LLM-based applications built on the client's own data and processes, with measured quality (evals).

Cloud
Amazon Bedrock (Claude and other models), or the Anthropic and OpenAI APIs directly. On Bedrock the data stays in an EU region.
On-prem
Open-weight models (e.g. Llama, Qwen) on the client's own servers, when the data can't leave the building.
  • Search and answers over internal documents (RAG)
  • Processing contracts, invoices and customer emails
  • Automating multi-step casework (agents)

Two-week AI validation

One specific AI idea, tested on the client's own data for two weeks. It ends with a measured go/no-go decision.

  • Use case and success criteria agreed up front
  • A working prototype in the client's environment
  • Measured results and an estimated running cost
  • Written recommendation on next steps

AI-native software development

Rolling out AI coding assistants (e.g. Claude Code) to development teams, with security and cost under control.

  • Tool selection and rollout plan
  • Permissions and guardrails for the assistants
  • Cost budget and ROI tracking

Area 2 of 3

Cloud and security

Infrastructure is written as code, in Terraform or AWS CDK.

AWS migration and landing zone

Moving applications onto a secure, multi-account AWS foundation (landing zone).

  • Assessment and migration plan
  • Well-Architected review of an existing environment
  • Accounts, network and security baseline (Control Tower, IAM Identity Center, GuardDuty, Security Hub)
  • Migration (AWS MGN, DMS) and tuning after the cutover

FinOps

A review of the AWS bill, then implementing the savings.

  • Costs by service and by team, with tagging and Cost Explorer
  • Rightsizing, shutting down idle resources, Savings Plans and Reserved Instances
  • Budgets and alerts in AWS Budgets, a monthly report for each team

DevSecOps and access management

Security checks in the delivery pipeline, and automated access management.

  • Role-based, auditable permission provisioning (RBAC) with emergency (break-glass) access
  • SSO and user provisioning with SAML and SCIM
  • EKS and Kubernetes hardening: Pod Security Standards, Kyverno
  • Container image scanning in CI with Trivy

Area 3 of 3

Engineering organization

Measuring and improving Developer Experience

A survey of where development slows down, followed by targeted fixes and a second measurement.

  • Developer survey with industry benchmarks
  • Delivery metrics: cycle time, lead time, deployment frequency
  • Bottlenecks in code review and CI

Fractional Head of DevEx / AI

A part-time or interim lead for Developer Experience or AI adoption, until a permanent hire is in place.

  • Roadmap and priorities
  • Reporting the return to leadership
  • Handover to the permanent lead

Contact

Talk about your team's project.

Describe the team and the problem in a few lines, and say when the work should start.

Email hello@vectislab.hu Or see the product