# AISynq

> AISynq is an AI consultancy. We find the AI opportunities worth building inside a company, build them, and measure whether the number moved.

AISynq is an AI consultancy. The practice is one method applied to three kinds of client, and the
third step is the part most firms skip: measuring whether the thing that was built moved a number.

## The method

- **Identify**: Two to three weeks. You get a ranked list of what is worth building, and a longer list of what is not.
- **Build**: Working software in your repository, running in your stack, reviewed by your engineers.
- **Prove**: The same number, measured before and after. If it did not move, the report says so.

## Who it is for

- **Startups** (https://aisynq.com/startups): Pre-launch to Series B. You want AI inside the product without hiring an AI team first, and you need the build to survive contact with real users.
- **Software firms** (https://aisynq.com/software-firms): Established product companies with real customers, real systems, and internal processes that take longer than anyone in the room would admit.
- **VCs and accelerators** (https://aisynq.com/funds): Funds and programmes that need a technical read on what they are about to back, and hands-on help for the portfolio afterwards.

## Proof

Work delivered for: DHL, AT&T, DirecTV, Accenture, Singapore Government project, Simons Group.
These were delivered on directly, in some cases through an employer or as part of a larger delivery
team, rather than as AISynq retainer clients. The flagship figure is roughly $100M in annual operational value at DHL scale, from the programme AISynq contributed to. A figure that size is never one person’s work.

## Key pages

- [What AISynq does](https://aisynq.com/services): the full service list, grouped by outcome, plus an explicit list of work the firm turns down.
- [How we work](https://aisynq.com/how-we-work): the three steps expanded, with what happens inside each.
- [Work](https://aisynq.com/work): case studies, each with a limitations section.
- [Free 48-hour technical due diligence memo](https://aisynq.com/funds): for investors evaluating a company. No fee.
- [About](https://aisynq.com/about): who runs it and the enterprise background.
- [Contact](https://aisynq.com/contact): a 30-minute call.

## Straight answers to common decisions

Each of these opens with a verdict written to be quoted on its own.

- [Should we hire an AI engineer or bring in a consultancy?](https://aisynq.com/compare/ai-consultancy-vs-hiring-an-ai-engineer)
  Hire when you already know what the person will build for the next year, because a permanent engineer is cheaper than any consultancy over that horizon and the knowledge stays in the building. Bring in a consultancy when you do not yet know what is worth building, when you need it decided in weeks rather than in a hiring process, or when the work is one programme rather than a continuing stream. The common expensive mistake is hiring first: a company hires an AI engineer, discovers three months later that the real bottleneck is a data quality problem or a rules engine, and now carries a salary against a job that turned out not to exist.
- [Should we build our AI feature or buy it?](https://aisynq.com/compare/build-vs-buy-ai-feature)
  Buy, unless the feature is the thing customers choose you for or the data behind it is something a vendor cannot get. Most AI features are not differentiators, they are table stakes, and building table stakes yourself means carrying evaluation, monitoring, model upgrades and failure handling forever in exchange for a feature nobody switches to you for. The test that settles most cases: if a competitor shipped exactly this feature next month using an off-the-shelf vendor, would you lose customers over it? If not, buy it and spend the engineering on the thing they cannot copy.
- [AI assessment or a strategy consultancy?](https://aisynq.com/compare/ai-assessment-vs-strategy-consultancy)
  Buy a strategy engagement when the problem is agreement. A large consultancy is very good at getting forty stakeholders to sign up to one direction, and that is real work that a small specialist cannot do. Buy an assessment when the problem is a decision. You get a ranked list of what is worth building, priced in hours and money, from people who will also build it. The failure to avoid is buying a strategy when you needed a decision. You get a document everyone agreed to and nobody can act on, because nothing in it was costed against your own systems.
- [Specialist AI team or a development agency?](https://aisynq.com/compare/ai-build-vs-development-agency)
  Use a development agency when the specification is clear and the work is software. They are cheaper per week, they scale to more hands, and a good one will build a well-tested product from a brief. Use a specialist when the hard part is not the software but the behaviour: whether the output is right often enough, what it costs per user at real volume, and what happens when it is wrong. The tell is in the quote. If the estimate has no line for an evaluation set, the agency has priced a feature and not a system.
- [Bespoke AI training or a course platform?](https://aisynq.com/compare/bespoke-ai-training-vs-course-platform)
  Buy a course platform when individuals want to learn on their own schedule and nobody is depending on a specific outcome. It is far cheaper per person, the content is usually good, and for a motivated individual it beats anything bespoke. Buy bespoke training when a team has to work differently afterwards, because that needs people in a room disagreeing about a real task from their own work. The mistake is buying seats for a hundred people and calling it a programme. What you have bought is a report showing how many of them logged in.

## Tools

- [Are your developers doing a good job?](https://aisynq.com/tools/build-health): free and entirely open. Fourteen signals a non-technical founder can check without reading code, each with how to check it and what to do when the answer is no. For founders who have raised and have no CTO.
- [Will ChatGPT quote your page?](https://aisynq.com/tools/aeo-readiness): free. Fourteen checks on one page: whether the answer-engine crawlers are allowed in, whether there is a liftable answer near the top, whether a machine can read the structure, and whether the claims carry sources. Returns a count of checks cleared rather than a score, because a score would imply a weighting nobody can defend.
- [What does it cost to build an AI agent?](https://aisynq.com/tools/ai-agent-build-cost): free. Returns the work split into engineering weeks by line item, converted by the reader's own weekly cost rather than by an invented currency figure. Names the four line items quotes routinely omit: the evaluation set, the failure handling, the cost controls and the handover.
- [Will your AI feature pay for itself?](https://aisynq.com/tools/ai-unit-economics): free calculator returning cost per user per month and a verdict against your own pricing and margin.
- [AI due diligence questionnaire](https://aisynq.com/tools/ai-due-diligence): free and entirely open, nothing behind a form. Technical questions to put to a company claiming AI, each with what a strong answer sounds like next to what should worry the reader.
- [How much does an AI support agent cost per user?](https://aisynq.com/tools/ai-unit-economics/ai-support-agent)
- [How much does RAG cost per query?](https://aisynq.com/tools/ai-unit-economics/rag-search)
- [How much does AI document summarisation cost?](https://aisynq.com/tools/ai-unit-economics/document-summarisation)
- [How much does AI code review cost per developer?](https://aisynq.com/tools/ai-unit-economics/ai-code-review)
- [How much does AI content generation cost per user?](https://aisynq.com/tools/ai-unit-economics/content-generation)
- [How much does AI data extraction cost per record?](https://aisynq.com/tools/ai-unit-economics/data-extraction)
- [What does an AI meeting notetaker cost per user?](https://aisynq.com/tools/ai-unit-economics/meeting-notes)
- [How much does AI classification cost at volume?](https://aisynq.com/tools/ai-unit-economics/semantic-classification)
- [How much does an AI agent cost per task?](https://aisynq.com/tools/ai-unit-economics/ai-agent-workflow)

## Case studies

- [DHL: The exception queue nobody could read](https://aisynq.com/work/dhl): How an operation at DHL scale found the gap between what its systems knew and what its people could see in time to act, and what it was worth.

## Writing

- [AI opportunity](https://aisynq.com/articles/ai-opportunity): How to work out which AI projects inside a company are worth doing, and which ones quietly are not.
- [Building with AI](https://aisynq.com/articles/building-with-ai): Shipping AI into production software: agents, automation, evaluation, and what it costs to run once real traffic arrives.
- [Technical due diligence](https://aisynq.com/articles/technical-dd): Evaluating an early-stage engineering team and codebase, written for the people writing the cheque.
- [AI training](https://aisynq.com/articles/ai-training): Teaching a team to use AI on its own work: what it costs, how to choose a provider, and how to tell afterwards whether it changed anything.
- [Buying AI help](https://aisynq.com/articles/buying-ai-help): A firm, a freelancer, an agency or your own team. What each really costs over a year, and how to tell a good one from a confident one.
- [Operator notes](https://aisynq.com/articles/operator-notes): Build logs and the specific decisions behind them, including the ones that went badly.

- [AI bootcamp, workshop or course?](https://aisynq.com/articles/ai-bootcamp-vs-workshop-vs-course): Three formats, what each one is actually for, and how to pick. Includes the case where the answer is none of them.
- [AI freelancer or a firm?](https://aisynq.com/articles/ai-freelancer-or-firm): A good freelancer is the best value here and the highest variance. How to spot one, and the three jobs where a firm is worth the premium.
- [How to automate a startup, piece by piece](https://aisynq.com/articles/automate-a-startup-piece-by-piece): The order to automate a small company in, why that order is the opposite of what most founders start with, and the test each piece has to pass first.
- [What corporate AI training costs](https://aisynq.com/articles/corporate-ai-training-cost): Published ranges for AI workshops, bootcamps and executive sessions, plus the six things that move the number and the two lines most quotes leave out.
- [What an in-house AI team really costs](https://aisynq.com/articles/cost-of-an-in-house-ai-team): The twelve-month cost model, including the six lines that never appear in the business case. Put your own salary figure in and the arithmetic is yours.
- [How to choose an AI consultancy](https://aisynq.com/articles/how-to-choose-an-ai-consultancy): A four-stage way to evaluate an AI firm, the questions that separate them in twenty minutes, and the five mistakes buyers make most.
- [How to choose an AI training provider](https://aisynq.com/articles/how-to-choose-an-ai-training-provider): Nine questions to ask a provider before you sign, what a strong answer sounds like next to a weak one, and the three claims worth ignoring entirely.
- [How to prove AI training worked](https://aisynq.com/articles/prove-ai-training-worked): Every provider agrees follow-up matters and almost none of them measure anything. A method that takes one afternoon before the session and one after.
- [Should a small startup build a software factory?](https://aisynq.com/articles/software-factory-small-startup): A software factory multiplies whatever discipline you already have. If you cannot tell whether your developers are doing a good job today, more output is worse.
- [Who in your company needs AI training first](https://aisynq.com/articles/who-needs-ai-training-first): Four groups, four different sessions, and why putting them in one room serves none of them. Plus the group most companies train last and should train first.
- [Which processes to automate with AI first](https://aisynq.com/articles/what-to-automate-with-ai-first): The candidates are the same at every software company. Four tests that predict which survive production, and the order to attempt them in.
- [How to add AI to your SaaS product](https://aisynq.com/articles/how-to-add-ai-to-your-saas-product): A sequence for adding an AI feature to a product that already has customers, and the four things that decide whether it survives its first month.
- [How to build an AI MVP](https://aisynq.com/articles/how-to-build-an-ai-mvp): The usual MVP advice applies, plus one question it does not cover. An AI MVP has to validate demand and accuracy, and most founders only test the first one.
- [How to prioritise AI use cases](https://aisynq.com/articles/how-to-prioritise-ai-use-cases): A six-step framework for ranking AI candidates, including the step every other framework skips. Where the business value number actually comes from.
- [How to measure a process nobody has ever measured](https://aisynq.com/articles/measuring-a-thing-that-was-never-instrumented): You cannot prove an AI project worked without a baseline, and most internal processes have none. Four ways to build one in a fortnight.
- [How to assess a startup codebase in two days](https://aisynq.com/articles/reading-a-codebase-in-two-days): What technical due diligence can establish in 48 hours, what it cannot, and the six questions that separate a platform from an API wrapper.
- [How to decide what AI not to build](https://aisynq.com/articles/the-second-list): Most AI opportunity assessments produce a list of things to build. The useful half is the list of things not to build, and here is how to produce one.
- [What happens when your AI agent gets it wrong](https://aisynq.com/articles/what-your-agent-does-when-it-is-wrong): Agents in production are judged on their failure path. What to build around the model so the eleventh real user does not find the edge your ten tests missed.

## Written for a specific situation

- [Your cohort is full of AI companies. How many of them have built anything?](https://aisynq.com/for/accelerator-ai-cohort): For accelerators and programmes running an AI-heavy intake, where the partners are technical enough to know they cannot check twenty companies themselves.
- [You shipped the AI feature. Nobody can say what it changed.](https://aisynq.com/for/ai-feature-shipped-no-numbers): For product teams whose AI launch went out months ago and has not appeared in a board deck since, because there was never a before to compare it to.
- [You rolled out AI to the whole company. Check who opened it last week.](https://aisynq.com/for/ai-licences-nobody-opens): For companies twelve months into a licence renewal they cannot justify, where the pilot went well and the adoption did not.

## Short positions

Each of these is a stated position rather than a summary, and each has a permalink.

- [The largest single win on a programme worth roughly $100M a year in operational value involved no model at all.](https://aisynq.com/feed#no-model-at-all) (Not building it)
- [Your AI prioritisation matrix scored that project 8.4. Ask the person who wrote the 8.4 where it came from.](https://aisynq.com/feed#scores-are-made-up) (Proving it worked)
- [The operators had already built the ranking. It lived in their heads and a spreadsheet three of them maintained. Nobody had ever asked them to write it down.](https://aisynq.com/feed#nobody-asked-them) (Proving it worked)
- [Per-seat pricing on a feature with a per-use cost breaks quietly, at exactly the moment customers start liking it.](https://aisynq.com/feed#per-seat-per-use) (What it costs)
- [The evaluation set is the cheapest line on any AI build estimate and the first one teams cut.](https://aisynq.com/feed#eval-set-cut-first) (Building it)
- ["We could switch model providers easily." When did you last try?](https://aisynq.com/feed#switch-models-easily) (Diligence)
- [Two quotes for the same AI build differ by five times. It is almost never the model work.](https://aisynq.com/feed#four-missing-lines) (What it costs)
- [Search for an AI due diligence checklist and you get a generic venture template with an AI heading pasted on.](https://aisynq.com/feed#dd-templates-are-generic) (Diligence)
- [You shipped it without taking a baseline. You can still build one afterwards, from data you already have.](https://aisynq.com/feed#baseline-after-the-fact) (Proving it worked)
- [A real share of what gets scoped as an AI feature is a rules problem, a search problem, or a form with too many fields.](https://aisynq.com/feed#a-form-with-too-many-fields) (Not building it)

## Optional

- [Full text of every page](https://aisynq.com/llms-full.txt): the same content in one file, for agents that would rather not crawl.
- [Notes for AI agents](https://aisynq.com/for-ai-agents): the human-readable version of this file.
- [Privacy](https://aisynq.com/privacy) and [Terms](https://aisynq.com/terms).