Concrete deliverableEvaluation report
01Task set
02Acceptance result
03Failure case
Create an assistant or product feature around your approved knowledge, access rules and acceptance tests. We first check whether an existing tool can meet the need before recommending custom development.
Choose the product or knowledge problem that feels familiar.
Ground the system in approved company knowledge, data and workflows.
Build retrieval experiences that surface relevant information from controlled sources.
Expose model capabilities through secure APIs and product interfaces.
Design agents or orchestration with defined tools, permissions and checkpoints.
Choose architecture, hosting and access rules around the data and risk profile.
Evaluate prompting, RAG, structured workflows or model customisation before choosing the heavier option.
Turn a clearly scoped business need into a prototype, connected knowledge flow and evaluated AI feature your team can test safely.
Build the smallest useful version around the real task before committing to a larger product build.
Give the feature controlled access to approved knowledge, APIs or business systems required for the task.
Test representative cases for usefulness, grounding, failure modes and required human intervention.
Track usage, exceptions and quality signals so the system can be improved with evidence instead of guesswork.
Illustrative only, not a client case study. Final scope and outputs are agreed in the proposal.
Core modules support knowledge-grounded assistants and controlled AI workflows. Predictive, vision, recommendation and anomaly models are related AI capabilities, scoped separately.
RAG System Development: A retrieval-based assistant that answers from approved documents and shows supporting sources.
Build the capability around your data, permissions and real acceptance criteria.
Choose the approach based on the task, data and knowledge access, evaluation requirements, latency and operating-cost constraints.
| What matters | Existing AI product | Custom AI development |
|---|---|---|
| Best fit | The product meets your task and access requirements | A tested need exceeds available product capabilities |
| Effort | Configuration and product subscription | Discovery, engineering, evaluation and maintenance |
| Control | Within the provider’s supported settings | Project-specific interfaces, access and evaluation |
| Decision | Use when it meets acceptance criteria | Build only when the added value justifies the work |
We support documented handover or ongoing support; deployment, data access and maintenance boundaries are set out below.
See a sample first pilotEvery deliverable supports a production decision: whether the system is useful, grounded, safe enough and operable at the agreed cost.
Off-the-shelf AI tools do not fit your data, your language or your edge cases
Your data is too sensitive to feed into someone else's platform
You want to own an AI asset, not rent another subscription
Grouped so you can see the scope without reading a catalogue.
You retain your source data. For a custom build, project code, configurations, prompts, evaluation assets and documentation can be handed over to your team. Third-party models, libraries and services remain under their own licences, and model-generated output is subject to the selected provider terms.
We connect only approved sources and tools, use role-based or existing identity controls where the integration supports them, and can use private deployment patterns where the architecture permits. Retention and deletion controls are configured in components we control where the selected platform supports them; third-party retention limits are reviewed before use.
Yes we regularly co-build with in-house teams and hand over clean, documented systems.
When it meets the task, access and quality requirements with less cost and maintenance. Custom development is justified only by a specific gap that we can test.
Send us the workflow, problem or idea you want to discuss. We’ll reply within one business day.
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