Custom AI copilot for an internal team
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Service
Custom AI Development
PlanckCyber builds custom AI software for organizations whose requirements are not well served by an off-the-shelf product. Work can include AI web applications, internal tools, copilots, embedded AI product features, APIs, retrieval systems, model integrations and purpose-built interfaces. The engineering approach starts with the user and business outcome, then selects the simplest architecture that can meet the requirement. PlanckCyber can integrate commercial or open models where appropriate without locking the product strategy to a single provider. Projects are designed for production concerns such as permissions, data handling, evaluation, observability, latency and cost. A project can begin as a direct build when requirements are clear, as a prototype to test feasibility, or with discovery when the product and business case still need definition.
Problems We Solve
When this service fits.
- Off-the-shelf AI tools do not fit the workflow
- A product needs differentiated AI capability
- Teams need a secure internal AI application
- Multiple AI and business systems must work together
- A prototype needs to become reliable production software
What We Can Deliver
Built around the requirement.
- Product and technical architecture
- Front-end and back-end application development
- Model, RAG and API integrations
- Authentication and authorization integration
- Evaluation and test systems
- Deployment and operational documentation
Use Cases
Concrete examples.
AI-enabled customer portal
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Embedded AI features for SaaS products
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Purpose-built research application
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Domain-specific document review tool
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
How an Engagement Can Start
Use the smallest responsible starting point.
Direct product build
For defined requirements, users and success criteria.
Prototype / proof of concept
To test product behavior or technical feasibility.
Product discovery
To turn an AI idea into a scoped, testable roadmap.
Process
From definition to evidence.
- Define users and outcomes
- Design experience and architecture
- Prove uncertain technical assumptions
- Build, test and integrate
- Release, observe and improve
Data, Integration & Security
Constraints are design inputs.
- Identity and access control
- Model/provider portability
- Data architecture and retention
- Evaluation and regression testing
- Performance, cost and observability
Related Solutions
See the service applied to a business problem.
Document Intelligence
Document-heavy processes require people to extract, compare, classify and review information that software can often assist with.
Explore solutionEnterprise Knowledge Assistant
Employees waste time searching across documents and systems, while generic AI tools may answer without reliable access to approved company knowledge.
Explore solutionCustom AI Products
A business has an AI product idea or wants differentiated AI features that do not fit a generic SaaS tool.
Explore solutionFAQ
Questions buyers ask.
Do you build complete applications or only the AI layer?
Both are possible. PlanckCyber can build the AI capability, the application around it, or integrate with an existing product and engineering stack.
Are we locked to one model provider?
Not by default. Architecture can be designed around business requirements and provider tradeoffs so model choices can evolve when that is technically and commercially sensible.
Can you turn an existing prototype into production software?
Yes. That usually requires reviewing architecture, data flows, evaluation, security, reliability and operating ownership before hardening the system.
Start with the problem
Have a problem AI might solve?
You do not need a specification. Tell us what you are trying to improve.