AI inside real workflows
Teams hire us when AI needs to support real work: research, support, decisions, coordination, or execution. Not as a demo, but as part of a workflow people rely on
9 YEARS DESIGNING COMPLEX WORKFLOWS
AI systems for teams moving beyond simple automation
We help product teams design, build, & integrate copilots, retrieval systems, & agentic workflows that hold up in real products and internal operations
Why teams hire us
Reliability before exposure
Many teams start with internal AI first. We help reduce manual glue, test leverage, & create safer access to knowledge, tools, & actions
2-3 HOURS-PER-WEEK TIME COMMITMENT
AI that users can trust
Once AI enters product workflows, behaviour matters as much as capability. We design for latency, uncertainty, fallbacks, permissions, & human override
12 AI COPILOTS DESIGNED
Why teams hire us
AI inside real workflows
Teams hire us when AI needs to support real work: research, support, decisions, coordination, or execution. Not as a demo, but as part of a workflow people rely on
9 YEARS DESIGNING COMPLEX WORKFLOWS
Reliability before exposure
Many teams start with internal AI first. We help reduce manual glue, test leverage, & create safer access to knowledge, tools, & actions
2-3 HOURS-PER-WEEK TIME COMMITMENT
AI that users can trust
Once AI enters product workflows, behaviour matters as much as capability. We design for latency, uncertainty, fallbacks, permissions, & human override
12 AI COPILOTS DESIGNED
Why teams hire us
AI inside real workflows
Teams hire us when AI needs to support real work: research, support, decisions, coordination, or execution. Not as a demo, but as part of a workflow people rely on
9 YEARS DESIGNING COMPLEX WORKFLOWS
Reliability before exposure
Many teams start with internal AI first. We help reduce manual glue, test leverage, & create safer access to knowledge, tools, & actions
2-3 HOURS-PER-WEEK TIME COMMITMENT
AI that users can trust
Once AI enters product workflows, behaviour matters as much as capability. We design for latency, uncertainty, fallbacks, permissions, & human override
12 AI COPILOTS DESIGNED
What we build
Copilot experience design
Define when AI should guide, suggest, ask, or stay quiet
Agent workflow design
Structure multi-step tasks across tools, states, & approvals
Tool calling & actions
Enable AI systems to interact with tools, APIs, & services
Human-in-the-loop control
Add approval points, override paths, & safer autonomy boundaries
State, memory, & recovery
Preserve context & recover cleanly from failure
AI fluent process
AI affects system behaviour and user expectations.
We design for both.
AI Copilots & Agents
Copilot experience design
Define when AI should guide, suggest, ask, or stay quiet
Agent workflow design
Structure multi-step tasks across tools, states, & approvals
Tool calling & actions
Enable AI systems to interact with tools, APIs, & services
Human-in-the-loop control
Add approval points, override paths, & safer autonomy boundaries
State, memory, & recovery
Preserve context & recover cleanly from failure
Knowledge & Retrieval
RAG system design
Shape retrieval around the content, task, & answer quality required
Knowledge access UX
Design how users ask, inspect sources, & build confidence
Retrieval & ranking logic
Improve answer quality through filtering, re-ranking, & context design
Source visibility patterns
Show where answers come from & what supports them
Knowledge in workflow
Embed retrieval into support, research, onboarding, & domain tasks
Conversational UX
Conversation flow design
Structure multi-turn interactions with better guidance & recovery
AI state & transparency
Design thinking, waiting, & error states users can understand
Voice & multimodal patterns
Support interfaces where text, voice, & visual context work together
Clarification & handoff
Decide when the system should ask, escalate, or return control
Domain-specific behaviour
Shape tone, pacing, & interaction rules for the product context
Internal AI Platforms
Internal copilots and assistants
Build AI support for research, documentation, triage, & repetitive work
Cross-tool orchestration
Connect workflows across CRM, support, docs, & internal systems
Decision & knowledge layers
Surface relevant context, signals, & suggestions inside workflows
Safer internal adoption
Start with internal leverage before customer-facing rollout
Operational fit & rollout
Design for repeated team use, not one-off experiments
AI Evaluation
Behaviour tracking
Monitor how outputs change across prompts, models, and use cases
Evaluation design
Define what good behaviour looks like for the product
Prompt & system comparison
Compare versions, workflows, & behaviours under realistic conditions
Failure pattern analysis
Identify recurring weaknesses in retrieval, reasoning, or UX
Improvement loops
Turn observations into systematic iteration rather than reactive fixes
AI fluent process
AI affects system behaviour and user expectations.
We design for both.
Operating philosophy
Behaviour before brilliance
Useful AI beats impressive AI
Failure-aware by default
Systems must absorb imperfect outputs
Trust is designed
UX & engineering shape confidence
Models will change
The system must survive churn
Context is the real work
Good AI depends on better inputs
Human override stays visible
Autonomy needs clear boundaries
How we work
- Unclear feasibility
- Fast model churn
- Trust constraints
- Limited bandwidth
- High expectation risk
FlexiPod Retainer
- One senior anchor
- Specialists as needed
- Scale stage teams with ongoing design needs
Fixed-Scope Projects
- Defined deliverables and timelines
- Best for - redesigns, systems, discrete initiatives
AI Sprint Zero
- Clarify the problem and map complexity
- Align constraints and define direction before major builds
How we work
- Unclear feasibility
- Fast model churn
- Trust constraints
- Limited bandwidth
- High expectation risk
FlexiPod Retainer
- One senior anchor
- Specialists as needed
- Scale stage teams with ongoing design needs
Fixed-Scope Projects
- Defined deliverables and timelines
- Best for - redesigns, systems, discrete initiatives
AI Sprint Zero
- Clarify the problem and map complexity
- Align constraints and define direction before major builds
How we work
- Unclear feasibility
- Fast model churn
- Trust constraints
- Limited bandwidth
- High expectation risk
FlexiPod Retainer
- One senior anchor
- Specialists as needed
- Scale stage teams with ongoing design needs
Fixed-Scope Projects
- Defined deliverables and timelines
- Best for - redesigns, systems, discrete initiatives
AI Sprint Zero
- Clarify the problem and map complexity
- Align constraints and define direction before major builds
Selected AI work
VIEW ALL
Designing Trust: How Tcules Helped OmnyAI Reframe their AI-Human Relationship
Reframed AI as collaborator, not decision-maker.
Outcomes
High Fidelity Prototypes, Design System, SaaS
- Modernise & Replatform
- AI-assisted workflows
Leveraging Generative AI for Survey Creation
The solution focused on leveraging AI in the survey creation process.
Outcomes
Design System
- Modernise & Replatform
- AI-assisted workflows
Let’s see if we are a fit!artefact access
A practical conversation to assess feasibility, trust risks, & where AI can create real leverage in your product or operations