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SERVICE 05
AI & Automation Adoption
AI / Automation
Turning “can AI do something here?” into part of how the work actually runs.
Move past the stage where you tried ChatGPT or Claude but it never quite stuck for the business. We design everything through to embedding AI into your operations.
Process
01
Workflow analysis & finding where AI fits
02
PoC & impact validation
03
Production integration & operations design
04
Scaling out & enabling your team to run it
Who this is for
Getting past the “we tried AI, but it never landed in the actual work” stage
PAIN 01
We tried ChatGPT and Claude, but can’t picture how to build them into our work
Individual productivity went up, but there’s no clear path to organization-wide impact. RAG and agents are still just words we’ve heard.
PAIN 02
We rolled out an AI tool, but no one uses it and it has become a formality
PAIN 03
Security and data-leakage concerns are holding us back from committing
We make sure it doesn’t end as just another PoC—we build it into the work.
Tell us what you’re facing →
The SYSTEMI approach
Building AI into the work itself
Rather than just deploying a tool, we slot AI into your operational processes and design it so the impact keeps showing up.
01
Workflow analysis & finding where AI fits
We observe how the work flows and pinpoint the steps where AI genuinely helps. It starts with telling apart the steps where expectations run too high from those where they run too low.
Output
Workflow map / AI opportunity map / ROI estimates / priorities
Output
PoC implementation / evaluation dataset / impact report / go/no-go call
02
PoC & impact validation
Prompt design, RAG builds, small-scale agent prototypes. We set the success criteria up front and make the proceed/stop call on the numbers.
03
Production integration & operations design
Once the PoC proves out, we embed it into your business systems and existing workflows—designing monitoring, model updates, and how to operate when things misfire.
Output
Production AI app / monitoring dashboard / fallback design / operations runbook
Output / What happens next
Rollout playbook / in-house knowledge / improvement loop / governance setup
04
Scaling out & enabling your team to run it
Once one success pattern emerges, we roll it out to other areas. We also handle the enablement work to turn AI adoption into an organizational capability you own.
What sets us apart
Can they go beyond the PoC and embed it in the work?
There’s no shortage of AI-adoption support, but few teams take responsibility all the way through to embedding it in operations.
| AI consultancies | AI tool vendors | Outsourced dev shops | SYSTEMI | |
|---|---|---|---|---|
| Depth of business understanding | △ Mostly strategy layer | △ Product fit only | ○ After the spec | ○ From observing the work |
| PoC to production | △ Tends to stop at PoC | △ Tool delivery only | ○ After spec is fixed | ○ PoC through production and ops |
| Security design | △ | ○ Product-dependent | ○ | ○ Designed to fit the work |
| Building self-sufficiency | ✕ Ongoing dependence | ✕ Ongoing dependence | △ Hand-off | ○ Alongside you to full in-house ownership |
AI × AI adoption
Using AI to accelerate AI adoption
Auto-structuring of workflow observations
We use Claude to structure interview recordings, documents, and Slack logs, then comprehensively surface where AI can be applied.
Faster prompt design
With Claude Code we rapidly generate prompts, RAG queries, and evaluation datasets—multiplying the number of iterations more than tenfold.
Agent builds
We build operational agents with the Claude Agent SDK—designing all the way to delegating “a whole sequence people used to do by hand” to AI.
Related cases
Where we make the difference
A mix of cases we can disclose and illustrative model cases.
Putting Claude Code at the core of the Ambish engagement to shorten development lead time
Challenge
They wanted to ship faster, but securing engineers was the bottleneck.
Result
AI pairing plus FDE delivered more than double the throughput at the same cost.
MODEL CASE
FDE in action
A “RAG-ify field-manual search” engagement, designed on the front line from observing the work to PoC to production operation
Why they reached out
They tried RAG but quality wouldn’t stabilize, and there was no sign it would be used in production.
What we worked out
Observed the work to narrow down the steps where AI helps, then phased it in while measuring quality against evaluation data.
See all cases →
DELIVERABLES
A look at what we produce on the front line
Sample structures of the documents we actually hand over—organized so they serve directly as decision input for the next phase.
DOCUMENT 01 — AI opportunity map (work × AI approach × ROI)
AI_Opportunity_Map_v1.0.xlsx
Work stepsEach step in the target department’s workflow and its time spent
AI approachRAG / agent / document generation / classification / summarization, etc.
Expected impactHours saved, quality gains, or new value created—whichever applies
Implementation difficultyData readiness, integration with existing systems, security requirements
PriorityAn adoption roadmap ordered by impact × feasibility
DOCUMENT 02 — Proposed architecture diagram
Example AI-in-operations architecture — RAG / agent / business integration
UI
Web / Slack / TeamsIntegrated into existing work UIsLLM
Claude (Bedrock)Enterprise LLM
Agent / RAG layerBusiness logicSearch
pgvector / OpenSearchVector searchBusiness data
S3 / SharePointInternal documentsCore DBStructured dataMonitoring
CloudWatch + eval dashboardQuality monitoring* Quality is continuously measured against an evaluation dataset, with fallback paths and human-review routes built in for misfires.
* Confidential data stays entirely within the VPC, in a configuration that is not used to train Bedrock.
* Confidential data stays entirely within the VPC, in a configuration that is not used to train Bedrock.
FAQ
Common questions about AI & automation adoption
We’re not sure how to put LLMs and generative AI to work in our operations.
We start with analysis—where in your processes AI actually helps—rather than leading with a tool. Instead of “AI everywhere,” we focus adoption on the spots where the upside is large and the side effects are small.
We’re worried about security and information leakage.
We build on enterprise model platforms like Bedrock and Vertex AI, in configurations where your data is not used for training. When handling internal data, a fully VPC-contained RAG setup is also an option.
It’s hard to get our staff to actually use AI.
Deploying a tool alone won’t make it stick. We support you all the way through use cases tied to concrete work scenarios, internal workshops, and FAQ preparation. Operations design is the key to AI that actually gets used.
Which part of our work should we start with?
The standard move is to start with repetitive work whose decision rules can be put into words. FAQ responses, internal knowledge search, meeting-minute summaries, and form generation are entry points where results tend to come easily.
Related services
FDE · Forward Deployed Engineering →
Data & Analytics Platform Builds →
DX Enablement →
Beyond “we tried AI, but…”
We stay alongside you all the way to embedding AI in the work. Tell us what you’ve tried and where it stalled.
Talk to us about AI adoption (free)