Pixelik
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Thinking · Field notes on production AI

Written from
the field.

What ships, what breaks, and what it takes to keep AI running in production. Published when there is something true to say.

TopicsForward-DeployedWhy AI StallsAI AgentsInternal ToolsProduction AIAutomationChoosing a Partner
T-56

Ownership is the security posture

The safest AI vendor leaves nothing behind: the system in your cloud, our access ended at handover, no copy kept. That shrinks what procurement reviews.

Security

16 Oct 2026 · 6 min

T-55

What does a forward-deployed engineer actually do?

Half fieldwork, half shipping: the honest day-to-day of embedded engineering, written for the buyer watching one work rather than the candidate applying.

Definitions

25 Sept 2026 · 5 min

T-54

From Palantir to the AI labs: a short history of forward-deployed

Three eras of the same discovery: software does not deploy itself. How a Palantir job title became the AI industry’s billion-dollar operating model.

Analysis

25 Sept 2026 · 5 min

T-53

Proof of concept vs production AI: different questions, not different polish

A PoC asks whether the model can. Production asks whether the organization can depend on it. Confusing the two is how 29 in 33 PoCs stall.

Definitions

02 Oct 2026 · 5 min

T-52

AI-native internal tools: judgment steps built in, not bolted on

The difference between a tool with a chat window and a tool with reading inside its workflow. What changes, what stays boring, and why owned wins.

Analysis

09 Oct 2026 · 5 min

T-51

From spreadsheet to system: how the migration actually goes

Map the workflow, not the grid. The five-stage migration, the error harvest nobody expects, and the parallel run that ends on a named date.

Playbooks

09 Oct 2026 · 5 min

T-50

AI observability for operators: watching for fluent failure

AI systems fail politely: confident, well-formed, wrong, with a 200 status. The four signals that catch it, sized for operations without a platform team.

Playbooks

16 Oct 2026 · 5 min

T-49

Why software ships in weeks now

The quarters were mostly translation and waiting. What actually compressed, what did not, and why discovery matters more at high speed, not less.

Analysis

02 Oct 2026 · 5 min

T-48

Big-consultancy alternatives for the mid-market

You were never going to hire McKinsey. The honest map of the tier built for operations that need working AI systems, not transformation programs.

Buying guidance

18 Sept 2026 · 6 min

T-47

Ship, don’t advise: the direct path to production AI

Most AI stalls because the delivery model is wrong. The forward-deployed approach that ships it, for mid-market and enterprise teams stuck in the same gap.

The model

18 Sept 2026 · 7 min

T-46

Fixed scope vs time and materials: the incentive tell

One model profits by finishing, the other by continuing. When each is honest, how fixed-scope survives AI uncertainty, and the clauses that matter.

Buying guidance

18 Sept 2026 · 5 min

T-45

Pilot purgatory, and how to leave it

The state where AI pilots neither die nor ship has a name, three tells, and one exit: replace the renewal decision with a binary one.

Analysis

18 Sept 2026 · 5 min

T-44

AI agents vs RPA: rules versus reading

RPA replays recorded actions; agents read and decide. Where each wins, what the breakage patterns reveal, and how mature stacks wire both together.

Definitions

18 Sept 2026 · 5 min

T-43

An AI agent for the support queue, designed for resolution

The split that works: the agent takes the repetitive majority end to end, humans keep the judgment minority, and nobody optimizes for deflection.

Playbooks

18 Sept 2026 · 5 min

T-42

The spreadsheet key-person risk: when the sheet has a keeper

One person knows why column Q is sacred. The two-week test that measures the risk, the triage that buys time, and the fix that removes it.

Analysis

18 Sept 2026 · 5 min

T-41

Golden datasets and golden conversations

The regression suite of AI behavior: what golden sets are, how the conversational variant catches what scores miss, and how ours saved a migration.

Playbooks

18 Sept 2026 · 5 min

T-40

Automation ROI, honestly

Four cost lines, benefits measured from a real baseline, and the redeployment test that separates recovered capacity from theoretical minutes.

Playbooks

18 Sept 2026 · 5 min

T-39

Why the AI labs embed engineers instead of shipping better docs

The companies with the best models spent billions putting engineers inside customers. What they learned that made documentation feel insufficient.

Analysis

18 Sept 2026 · 5 min

T-38

Forward-deployed engineer vs solutions engineer

One is paid to win the deal, the other to make it real afterwards. The comparison, the architect variant, and the question buyers should ask vendors.

Definitions

11 Sept 2026 · 5 min

T-37

Forward-deployed engineering for SMBs: the tier the billions skipped

AWS, OpenAI, and Anthropic built forward-deployed engineering for enterprises. The model fits smaller operations better; only the delivery changes.

Analysis

11 Sept 2026 · 6 min

T-36

What AI-for-SMB studios actually promise

We reviewed 14 studios that AI search recommends for SMB AI development. Only 21 percent promise you own the code, and a third target enterprise, not SMBs.

Market data

11 Sept 2026 · 6 min

T-35

Anatomy of an embedded engagement, week by week

Days of discovery inside the workflow, a production build in weeks, hardening where reality demands it, and the handover that ends the dependency.

Playbooks

11 Sept 2026 · 6 min

T-34

Fifteen questions to ask an AI vendor, with the answers to listen for

Five groups, fifteen questions, and what good and bad answers sound like. Print it, ask them in order, and watch the market sort itself.

Buying guidance

11 Sept 2026 · 6 min

T-33

Who owns the code? The default answer surprises most buyers

Under UK law the contractor owns commissioned work unless assigned in writing. What that means for AI systems, where the code is the smallest part.

Buying guidance

11 Sept 2026 · 5 min

T-32

AI vendor red flags: what you will hear, and what it means

Nine phrases that sound reassuring in a sales call and predict trouble after it, each with the translation and the counter-move.

Buying guidance

11 Sept 2026 · 5 min

T-31

What AI agents cannot do yet, from people who run one

Five honest limits, each with its design implication. Written by operators of a production agent, not by a vendor with a quota.

Analysis

11 Sept 2026 · 5 min

T-30

The AI deployment gap

Models keep clearing benchmarks while 95 percent of organizations see no measurable return. The gap between those facts has a shape: three distances, closable.

Definitions

11 Sept 2026 · 5 min

T-29

Models retire. Plan for it.

Every LLM system inherits its provider’s deprecation calendar. The lifecycle, the migration playbook, and the incident that taught us to diary the dates.

Playbooks

11 Sept 2026 · 5 min

T-28

What "production AI" actually means

Everyone claims production AI. The term has a checkable definition: six properties that separate a system real users depend on from a demo with traffic.

Definitions

04 Sept 2026 · 6 min

T-27

AI workflow automation for operations: where it pays and where it lies

What AI adds to classic automation, the queue patterns where it earns its keep, and the honest hierarchy of fixes that starts before any tool.

Definitions

04 Sept 2026 · 6 min

T-26

AI agent vs chatbot vs copilot: which one are you actually buying?

Three words vendors use interchangeably, three different machines. The comparison, the tells, and the test that exposes what is really being sold.

Definitions

04 Sept 2026 · 5 min

T-25

Build vs buy AI agents: a decision framework for operations

Buy where your workflow is ordinary, build where it is the business. The signals that decide it, the hybrid that usually wins, and the trap in between.

Buying guidance

04 Sept 2026 · 6 min

T-24

An AI agent for lead qualification: notes from running one

Our inbound funnel has no human in it: an agent interviews, briefs, and books. The design choices, the operating lessons, and what transfers to your intake.

Playbooks

04 Sept 2026 · 6 min

T-23

When to replace spreadsheets with software

The spreadsheet was the right tool until it quietly became the system. The five triggers that mark the crossing, and what the replacement looks like now.

Playbooks

04 Sept 2026 · 6 min

T-22

Eight signs your operation outgrew its spreadsheets

The version-named file, the person who cannot take holiday, the numbers nobody trusts: the signs that a sheet quietly became a system, and what each costs.

Analysis

04 Sept 2026 · 5 min

T-21

What to automate first: a scoring method for real queues

Volume times structure times stability: the three-factor score that ranks your queues, the traps that invert it, and the decision path on one page.

Playbooks

04 Sept 2026 · 5 min

T-20

When no-code automation breaks: the five walls

Zapier-class tools are the right first move and the wrong permanent one. The five walls where visual automation ends, and what graduating actually means.

Analysis

04 Sept 2026 · 5 min

T-19

AI agents for business operations: a working guide

What an AI agent actually is, where agents pay for themselves in operations, how to spot agent-washing, and what reliability takes. Written by operators of one.

Definitions

28 Aug 2026 · 6 min

T-18

Custom internal tools vs SaaS: the build-or-buy math changed

SaaS won the last decade by default because building cost quarters. Building now costs weeks. When each side wins, and the middle trap to avoid.

Analysis

28 Aug 2026 · 6 min

T-17

From AI pilot to production: the crossing, step by step

Most pilots never make it: IDC counted 4 in 33. The six-step crossing that gets an AI system into production, written for operations-scale teams.

Playbooks

21 Aug 2026 · 6 min

T-16

AI project failure statistics: every number, sourced and reconciled

95%, 88%, 80%, 42%, 40%, 30%: the AI failure statistics quoted everywhere, what each one actually measured, and why they do not contradict each other.

Analysis

21 Aug 2026 · 6 min

T-15

AI consultancy vs agency vs freelancer vs in-house

The four classic ways to get AI built, compared on deliverable, cost structure, timeline, and ownership, plus the fifth option the comparison usually omits.

Buying guidance

28 Aug 2026 · 6 min

T-14

Forward-deployed engineer vs fractional CTO vs agency vs in-house

Four ways to put senior engineering judgment into your operation, compared by role: who does what, what each hands over, and which gap each one fills.

Buying guidance

28 Aug 2026 · 5 min

T-13

Vibe coding vs production grade: where the line actually is

AI-assisted development is why software ships in weeks now. It is also generating insecure code at scale. Both are true; the difference is the wrapper.

Analysis

28 Aug 2026 · 6 min

T-12

Choosing an AI development studio in London

The London AI market in three tiers, whether local actually matters, the tests that cut through studio marketing, and where we sit in it, honestly.

Buying guidance

28 Aug 2026 · 5 min

T-11

What is a Workflow Audit?

Thirty minutes, one workflow, real numbers: what happens in a Workflow Audit, what you leave with, and what it deliberately is not.

Definitions

28 Aug 2026 · 4 min

T-10

The billion-dollar validation of forward-deployed engineering

In ninety days, OpenAI, AWS, and Anthropic each launched forward-deployed organizations. What the capital influx means if you are not an enterprise.

Analysis

21 Aug 2026 · 5 min

T-09

Why AI pilots fail

MIT found 95 percent of organizations investing in generative AI see no measurable return. The failure modes behind it, and what the 5 percent do differently.

Analysis

21 Aug 2026 · 5 min

T-08

How to choose an AI development partner

The engagement models compared, the nine questions that expose vendors, the ownership terms that matter, and the red flags. A buyer field guide.

Buying guidance

21 Aug 2026 · 7 min

T-07

Forward-deployed engineers as a service

You cannot hire a lab-grade FDE, and you do not need to. How embedded engineering is bought as a fixed-scope service, and how to judge who sells it.

Buying guidance

21 Aug 2026 · 5 min

T-06

The fractional AI engineer, explained

What a fractional AI engineer is, how the model differs from forward-deployed and fractional CTOs, what it costs in structure, and when each fits.

Definitions

21 Aug 2026 · 4 min

T-05

Is forward-deployed engineering just consulting, rebranded?

The skeptics have a point worth taking seriously. The honest answer, and the one test that separates the discipline from the costume.

Analysis

21 Aug 2026 · 5 min

T-04

The ownership handover: what you should hold when the vendor leaves

Code alone is not ownership. The six-part handover standard for AI systems: code, prompts, infrastructure, evals, credentials, runbooks, and how to demand it.

Playbooks

21 Aug 2026 · 5 min

T-03

LLM evals, explained for the people paying for the system

Evals are how an AI system proves it works. What they are in plain language, why vendors skip them, and exactly what to demand in your handover.

Definitions

21 Aug 2026 · 6 min

T-02

Our sales funnel is an AI agent. In June, it silently died.

A field note on running production AI on ourselves: the model retirement that killed our intake chat, and the checklist we now apply to every system we ship.

Field notes

12 Aug 2026 · 6 min

T-01

What is forward-deployed AI engineering?

The working definition of the discipline, where the term comes from, why the largest AI companies adopted it in 2026, and how to buy it well.

Definitions

12 Aug 2026 · 7 min

02 · Start

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