giip
SES Proposal
GIIP FDE EXPERIENCE HARNESS

Same AI. Different results.

GPT, Claude, Gemini — today, anyone can use a top-tier AI model. So why do people still get very different results from the same model? The quality of AI-driven work isn’t decided by model performance alone. What you check, how you judge it, what you execute, and where you stop — the experience and judgment built into that harness is what decides the outcome.

It’s not the model. It’s the Harness.

Not theory — experience built up in production.

GIIP FDE’s harness isn’t built on AI-generated generalities. It’s built on real experience facing design, incidents, performance problems, scaling and cost optimization in large-scale production environments.

40M

DAU

Experience running a service at 40M DAU scale

600K

Concurrent Users

Up to 600K concurrent connections

300M / day

Transactions

300M transactions per day at scale

1.76 PB

Storage

Petabyte-scale storage operations

300 Gbps

Traffic

Large-scale delivery infrastructure

¥200M / year

Cost Reduction

Roughly ¥200M/year in infrastructure cost reduction delivered

See profile & track record

Not a single owner — an AI FDE and a team of experts, operating together.

With a human FDE, the customer experience can hinge on one person’s experience and condition on any given day. With GIIP, a dedicated AI FDE observes your environment 24/7 and keeps investigating, executing, recording and improving. For moments that call for expert judgment — design decisions, incident root causes, high-risk changes — multiple TAMs review together. Those conclusions don’t end as a one-off answer. They accumulate as policies, runbooks and validation criteria inside your dedicated Harness.

You’re not renting one engineer. You’re getting an AI FDE that keeps learning from the experience of an entire team of experts.

CategoryPalantir AI FDEOpenAI’s agent operations platformGIIP AI FDE
Core focusBuilding data and applications inside FoundryDeploying and governing enterprise task agentsRunning your development, database, cloud, infrastructure and production operations
AI’s roleCarries out work on the Palantir platformOperates task agents the customer builtContinuously observes, investigates, executes and validates each customer’s environment
Expert involvementCentered on platform adoption and build-outDesigned by the customer or a partnerA group of TAMs reviews every high-risk decision
What customers getAI applied inside a data operations platformA foundation for running general-purpose agents24/7 AI operations plus accumulated senior operating experience

Palantir is genuinely an AI FDE built for data transformation, code repositories and ontologies inside Foundry, and OpenAI is a general-purpose agent operations foundation with policy, approval, guardrails and escalation. What sets GIIP apart isn’t “a better AI” — it’s that AI has been placed inside the accountability structure of running a customer’s infrastructure.

Using the best AI doesn’t automatically get you the best result.

AI model

Knowledge, reasoning, coding ability, writing, tool calling. Anyone can use the same model.

Harness

What to check, what to question, in what order to investigate, which tools to use, what to execute, where to require human approval, what to validate afterward. This is where experience makes the difference.

If the model is the “brain,” the harness is “how the work gets done.”

Not just a workflow automation tool. An engineering AI that owns your production service end-to-end — feature development, real-time incident response, and database tuning.

Palantir AI FDEOpenAI Agent PlatformGIIP AI FDE
The essence of the productA closed data platform (works only inside Foundry)Assembly-kit agent parts (DIY) (the customer builds the workflow themselves)A senior engineering AI you can deploy immediately (connects directly to the customer’s existing infrastructure and code)
Scope of work coveredOntology definitions, data pipeline transformationInternal chatbots, document summarization, simple task automationFeature development & deployment, 24/7 production monitoring, incident root-cause analysis and recovery, DB/query/infrastructure performance tuning
Level of AI actionCalling internal platform functions and running scriptsAnswering and running tools based on prompt instructionsTracing logs/metrics → diagnosing the root cause of bottlenecks → drafting code/DB fixes → executing safely and validating
Source of engineering knowledgeCentered on how to use the Palantir platformNo inherent knowledge (the customer embeds their own internal docs)Nearly 30 years of production know-how — from large-scale traffic, incidents, and cost reduction — built directly into the Harness
Handling incidents and high-risk situationsVendor support in case of platform failure (separate SLA)The AI isn’t accountable if something goes wrong (customer bears the risk when guardrails fail)High-risk work is automatically blocked, and a team of senior TAMs steps in immediately to review and resolve it
What customers ultimately feel“We paid a lot to adopt a new data platform.”“Now we have to build yet another agent ourselves.”“We got a team of senior engineers who handle development, incidents, and tuning around the clock.”

Palantir is powerful within its own platform, and OpenAI gives you the parts to build an agent yourself. But what you really need on the ground is a real engineer who understands your codebase, tracks down incidents at 3am, and tunes your slow queries and infrastructure. GIIP AI FDE is built on execution rules (a Harness) accumulated over nearly 30 years in production, and owns your service end-to-end — from development to operations, incident response, and tuning.

From an AI that answers questions, to an AI that runs Production.

General AI Assistant

  1. Question
  2. Answer

Coding Agent

  1. Instruction
  2. Coding
  3. Test

GIIP FDE

  1. Observe
  2. Diagnose
  3. Plan
  4. Risk Check
  5. Execute
  6. Validate
  7. Monitor
  8. Improve

GIIP FDE doesn’t stop at writing code. It’s designed so the scope of work covers the service actually running — and staying stable afterward.

Nearly 30 years of experience, turned into rules

Nearly 30 years of experience — not a prompt, but execution rules.

The judgment we built up over decades in production doesn’t stay as tribal knowledge. It’s encoded into the harness as concrete, repeatable rules.

  1. 1

    Investigation

    What to check first, which logs, metrics and queries to look at.

  2. 2

    Diagnosis

    An investigation order built to find the root cause, not just the symptom.

  3. 3

    Architecture

    Design judgment that accounts for performance, availability, cost and operability together.

  4. 4

    Production Safety

    Judging which changes are safe to execute and which require human approval.

  5. 5

    Performance

    Bottleneck analysis across database, application and infrastructure.

  6. 6

    Continuous Improvement

    Not stopping at execution — checking the result and feeding it into the next improvement.

You don’t need to write a clever prompt.

Just ask, the way you’d ask a human engineer on Slack.

“The DB has been slow since yesterday. Find the cause and fix it.”
“Deploy this GitHub repo to production on AWS.”
“Fix this error, test it, and ship it to production.”
“Traffic has been growing — review the architecture.”
“Our AWS bill is too high. Find savings without hurting performance.”
“Add this feature and give me a URL to check it.”
“Find the cause of yesterday’s incident and make sure it doesn’t happen again.”

You don’t need to become an AI expert.

AI is a tool whose output can vary a lot with the prompting skill of the person using it. But adopting an FDE doesn’t mean every employee at your company needs to become an AI expert. When the quality isn’t what you expect, GIIP checks and adjusts the model, prompts, tools, knowledge and execution procedures on our side.

Getting the most out of the FDE isn’t your job. Continuing to grow the FDE into something that works well is GIIP’s job.

This experience, available monthly — not by the person-month.

Could you hire an infrastructure engineer with nearly 30 years of production experience, available 24 hours a day, for $4,200 a month?

GIIP FDE Box is not a service that keeps one human engineer on-site 24 hours a day. It’s a service where an AI FDE — built on production engineering experience and judgment turned into a harness — keeps executing work continuously, with a senior FDE stepping in when needed.

FDE Box Full-Operation Plan

Monthly$4,200excl. tax

Requests any time, 24/7/365

No limit on the number of requests

No extra quotes for GIIP’s work

Dedicated FDE Box hardware included
No setup fee
Installation included
Environment configuration included
Repository, cloud, DB and other integrations
Development
Database
Infrastructure
Production operations
FDE Box monitoring
FDE Box incident response
Answer-quality tuning
Continuous harness improvement
Senior FDE support
No extra person-month charges for GIIP’s work

Work is executed in order of scale, risk and priority. High-risk operations such as production changes or data deletion follow the approval policy. Third-party usage fees (AWS, Azure, external AI APIs, SaaS, commercial licenses, etc.) are billed separately.

Same GPT.

Same Claude.

Same Gemini.

The results still aren’t the same.

Use the difference nearly 30 years of production experience makes — built into the Harness, not the model.

Nearly 30 years of experience, as your company’s AI FDE.

contact@littleworld.net

Frequently asked questions

Why do results differ even with the same AI model?

It’s not only about model performance. What you check, what order you investigate in, which tools you use, where you require approval, and what you validate afterward — this harness is what changes the outcome of the work.

What is GIIP FDE’s Harness?

GIIP FDE’s harness is the execution layer that connects AI models to infrastructure, databases, cloud, repositories and monitoring tools, and keeps investigation, judgment, execution, validation and improvement running continuously. It reflects judgment criteria and operational know-how built up over roughly 30 years of production engineering.

How is this different from ChatGPT or Claude Code?

ChatGPT and Claude Code are extremely capable AI models on their own. GIIP FDE is not a model — it’s a harness and operating environment that uses multiple AI models and tools together to continuously run investigation, development, database work, infrastructure, deployment, monitoring, incident response and improvement in a production environment.

Do I need AI expertise to use this?

No. You can request work the same way you’d ask a regular engineer. If the results don’t meet expectations, GIIP checks and adjusts the model, prompts, tools, knowledge and execution procedures.

How much does FDE Box cost?

$4,200 per month (excl. tax). This includes dedicated FDE Box hardware, installation, environment setup, continuous operation, FDE Box incident response, answer-quality tuning, and senior FDE support. There are no additional per-request quotes or extra person-month charges for GIIP’s work.

Can I request work 24 hours a day?

Yes, requests to the FDE can be made 24/7/365 with no limit on the number of requests. Work is executed in order of scale, risk and priority, and high-risk production operations follow the approval policy.

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