AI & Intelligent Automation

Useful AI, wired into real work.

We design assistants, RAG systems and agent workflows that fit operations, respect data boundaries and stay governable after launch.

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Best starting point

A repeated workflow with clear owners, enough signal in the data and a real cost to doing the work manually.

First move Workflow + data audit
Works best with Named data owner
Used with
OpenAI OpenAI ChatGPT ChatGPT Gemini Gemini Python Python TypeScript TypeScript AWS Lambda AWS Lambda Azure Azure AI OpenSearch Vector Search GitHub Copilot Copilot Docker Docker

What changes

We focus on places where AI makes operations sharper, not louder.

Private knowledge becomes searchable.

Internal documents, tickets and procedures stop living in separate silos and start supporting faster decisions.

Manual triage becomes structured.

Inbox review, intake and first-pass routing turn into workflows with visible logic and cleaner ownership.

Automation gets a human checkpoint.

The system moves quickly without hiding the moments where approval, auditability or escalation still matter.

How we move

The goal is to get one useful system live before the effort turns into a vague innovation programme.

Scan

We review the workflow, current tools and private data boundaries to find the best high-leverage entry point.

Shape

We define one bounded use case, the operating rules around it and the minimum architecture needed to support it safely.

Ship

We launch with monitoring, review loops and a clearer path for whether the system should expand or stay focused.

Core moves

The work is less about a flashy interface and more about how intelligence plugs into the operating model.

Map Retrieve Act Measure
Map the workflow

Start from the pressure, not the model.

We look at where time is being lost, where people are re-reading the same material and where an answer or decision could move faster with structured support.

Typical outputs
  • Workflow map with decision points
  • Data source inventory
  • Priority use case shortlist
Useful when
  • The team knows the pain but not the safest entry point
  • Several AI ideas exist and one needs a rational starting order
Build retrieval

Make the right context available.

RAG only works when the retrieval layer is deliberate. We connect internal knowledge sources, reduce noise and keep access boundaries aligned with the business.

Typical outputs
  • Document ingestion and chunking patterns
  • Scoped access rules
  • Prompt and retrieval testing loops
Useful when
  • Answers are hidden across policies, tickets or vendor docs
  • Teams need AI without opening up sensitive knowledge broadly
Add action

Move from assistant to operational flow.

We design agent-assisted steps for triage, drafting, routing and follow-up while keeping clear points for review, approval and escalation.

Typical outputs
  • Agent workflows with guardrails
  • Tool connections to CRMs, inboxes or internal systems
  • Fallback paths for human intervention
Useful when
  • Manual handoffs are slowing the service team down
  • There is value in speed, but not at the cost of accountability
Prove value

Keep the rollout attached to operations.

We define metrics early so the system is measured against response time, throughput, error reduction or another business outcome that matters beyond the demo.

Typical outputs
  • Acceptance criteria and guardrail metrics
  • Usage and review dashboards
  • Iteration backlog for the next phase
Useful when
  • Leadership wants evidence before scaling AI wider
  • The team needs to know what good looks like after launch

Best fit

AI adds the most value when the target workflow is real, repeated and worth governing properly.

Strong fit

  • Knowledge work is slowing teams down Search, triage or first-pass review keeps pulling people away from higher-value work.
  • The business wants speed with control Data boundaries, approvals and auditability matter as much as the model choice.
  • There is enough process stability The workflow is clear enough to automate without guessing what good looks like.

Not the first move

  • The workflow itself is still undefined If the team has not agreed how the process should run, AI will only mirror the confusion.
  • No owner exists for the underlying data Retrieval systems break down quickly when source quality and permissions are unmanaged.
  • The goal is only a public demo We are most useful when the system needs to survive daily use, not just impress once.

Want AI to solve a real workflow?

We can review the process, the data boundaries and the safest high-leverage place to begin.

Best first workshop Workflow and data review
Common next step Bounded RAG or assistant pilot
Talk to SISI See AI use cases