The problemStill retyping invoices and order emails into your system by hand, every day?
In shortAI integration services connect a language model to software you already run, so it can read invoices, sort email or answer questions from your own documents. We build them from $2,500 in 2–6 weeks for teams stuck with manual data entry, measuring accuracy on your real examples and capping usage costs before launch.
Every day someone on your team opens a PDF invoice, an order email or a form and types what it says into another system. It is slow, the typos flow into accounting, and month-end waits on it. AI integration services put a language model to work on exactly this kind of job, inside the software you already run.
The difference between a demo and a feature is testing. We build a set of your real examples first, measure how often the AI gets them right, and quote the production build only when the numbers are good enough for the job. Low-confidence results go to a person, and usage costs are capped before launch, so nothing fails quietly or bills you by surprise.
Maintained by Haider Ali, Founder & CEO · Updated
The problems that bring people to AI integration, in close to the words they use.
Hours of staff time every week, typos that flow into accounting and inventory, and a month-end close that waits on data entry.
What we doAI reads the PDF or email into structured fields, rules check totals and dates, and only low-confidence fields go to a person for approval before anything is saved.
You pay for automation and for manual checking at the same time. Template tools can also return empty fields with no visible error when a supplier changes its layout.
What we doWe measure field-level accuracy on your own documents before launch, use extraction that does not depend on layout, and alert you when a run fails instead of failing silently.
Staff interrupt colleagues or dig through folders for answers, or paste internal files into consumer ChatGPT and get generic replies.
What we doSearch over your own content (RAG): answers come from your documents, cite the passage used, and respect who is allowed to see what.
A wasted pilot budget, and a team that stops trusting AI, so the project is quietly shelved.
What we doThe evaluation set reruns on every prompt or model change, with monitoring, retries and a fallback to a person when results drop below the bar.
Usage costs eat margins. Runaway agents and retry loops can multiply a bill overnight, and many teams only find out when the invoice arrives.
What we doKeys stay on the server, per-user and monthly caps are set, cost per task is measured in testing, and smaller models are used wherever they pass the test set.
Confidential data leaves the company, client contracts may be breached, and blocking AI outright throws away the time it saves.
What we doA sanctioned internal tool on paid business API tiers, sensitive fields masked before they leave your systems, usage logged, and open models on your own hardware when data must stay in.
We would rather tell you now than take the project and disappoint you.
| Starts from | $2,500 for a focused job such as invoice extraction or email triage |
|---|---|
| Typical timeline | 2–6 weeks: test set in week 1, prototype numbers by week 2, production in weeks 3–6 |
| Best for | Teams that retype, sort or search documents by hand |
| Not for | Tasks that must be right every time with no human check |
| What you get | AI inside your software, an evaluation set, cost caps, logging and monitoring |
| What you own | The code, prompts, evaluation set and the AI provider account |
| Systems we connect | Anything with an API, such as QuickBooks, Xero, HubSpot, Shopify, Google Workspace, Microsoft 365 or your own database |
| How pricing works | Prototype numbers first, then a fixed quote. Product features with RAG or agents usually run $6,000–$20,000 |
Worth knowing: Model choice matters less than people think, and it changes every few months. What lasts is the evaluation set: a few hundred real examples with known right answers. With one, switching to a cheaper or better model takes an afternoon of testing instead of a leap of faith. Without one, nobody can tell you whether the AI is 90% right, or which 90%.
Real examples from your business with the correct answers, used to measure accuracy before and after every change.
Invoices, forms, contracts and emails turned into structured fields and checked against rules before they are saved.
OpenAI, Anthropic or Google models called from your app, CRM or back office on the server, with your own keys.
Retrieval-augmented generation (RAG) that answers from your documents and cites the passage it used.
Models that look things up or take actions through a small set of permitted tools, with approval steps on anything risky.
Per-request and monthly caps, caching, and smaller models wherever they are good enough, so the bill holds no surprises.
Paid business API tiers that do not train on your data by default, sensitive fields masked before they leave your systems, and logs you control.
Monitoring, plus a rerun of the evaluation set when a provider changes or retires a model.
A 22-second look at an AI integration we build: invoices arrive by email, AI extracts the fields, and low-confidence results go to a person before they post. From $2,500, 2–6 weeks.
Still retyping every invoice into your system by hand? Hours of staff time, every week. Typos flow into accounting. The demo worked. Production broke. There’s a better way. AI integration that reads invoices, with checks built in. Document and data extraction. Tested on an evaluation set. Cost limits and privacy by default. From $2,500 · 2–6 weeks · fixed quote. Get a fixed quote. pixelandoak.com/services/ai-integration
The reliable way to automate data entry is not to trust the AI blindly. It is to let the AI do the reading, let rules do the checking, and let a person approve only the fields the system is unsure about. This is the pattern we build:
We report field-level accuracy on your own documents before you pay for the production build, so you know how much checking is left.
A custom build is not always the right answer. If a ready-made tool covers your documents and your systems, use it. Here is how the usual options compare.
| Option | Good for | How you pay | Trade-off |
|---|---|---|---|
| Template parser (e.g. Docparser) | A few suppliers with fixed layouts | Monthly plan with page or credit limits | Breaks when a layout changes, sometimes without an error |
| AI document tool (e.g. Nanonets) | Many layouts and standard fields | Monthly plan or per page | You may still check fields; custom rules and connections are limited |
| Zapier or Make with an AI step | Simple flows between common apps | Per task, rising with volume | Hard to measure accuracy; cost grows with every run |
| Custom integration (us) | Your rules, your systems, measured accuracy | Fixed build from $2,500, plus model usage at your provider's rates | Higher cost up front |
It depends on which ChatGPT. The free and personal consumer apps may use conversations to improve their models unless a user turns that off, and staff rarely do. Paid business tiers and the APIs from OpenAI and Anthropic do not train on your data by default. Google's paid Gemini API tier works the same way, but its free tier may use data to improve products. (Provider terms checked October 2026; they change, so we re-check them before every launch.)
The practical fix is a sanctioned tool your team actually wants to use: built on a paid API tier, with names, account numbers and other sensitive fields masked before they leave your systems, and every request logged. When data must not leave at all, we run open models on your own hardware.
Portable by design, so you can move to a better or cheaper model without a rebuild.
OpenAI, Anthropic, Google, and open models where they fit better
Postgres with pgvector, or a hosted vector database when scale needs it
TypeScript or Python services, queues and retries, n8n for simpler flows
Evaluation sets with known answers, run on every change
Open models on your own hardware when data cannot leave
Vercel, Cloudflare Workers, or your own servers
Not sure whether an AI integration is what you actually need? Send over what you are trying to fix and we will tell you straight — including if a smaller job would do it.
Ask us first →A single task with clear inputs and a clear right answer, chosen for value, plus real examples with known answers so we measure before we promise.
Accuracy, speed and cost per task reported in numbers. The production build is quoted only after this.
Limits, logging, fallbacks and a human check where mistakes cost money. Haider reviews the numbers and signs off before launch.
The test set reruns when a provider changes or retires a model, so you see the effect before your users do.
AI agents from our Engineering and Quality & Security divisions do the work, in parallel. Haider Ali, founder and CEO, agrees the plan with you and signs off everything before it reaches you.
Real work that uses the same skills. Every card links to something you can open and inspect yourself.
Quotes our published prices from a facts file, never invented ones, and emails Haider the conversation the moment a visitor leaves an email. Try it: it opens on this page.
AI toolingWhich graphics card holds which open model, how fast it runs, and what each token per second costs.
Focused integrations such as invoice extraction or email triage start at $2,500. Features inside a product with search over your data, agents or several models usually run $6,000–$20,000. You see prototype accuracy and cost numbers before the production quote. Model usage is billed to your own provider account, with no markup from us.
Usually 2–6 weeks. Week one goes on choosing the job and building the test set, week two on a prototype with measured accuracy and cost, and the remaining weeks on the production build with limits, logging and fallbacks. A single extraction job sits at the short end.
You usually do not need to train it. The better approach is retrieval-augmented generation (RAG): the model looks up the relevant passages in your documents before answering and cites them. It is cheaper than training, stays current when your documents change, and can respect who is allowed to see what.
Not on paid business API tiers from OpenAI, Anthropic or Google, by default. Consumer apps and some free tiers may use data to improve their models. We build on paid API tiers, mask sensitive fields before they leave your systems, and re-check provider terms before each launch, because they change. Terms checked October 2026.
We limit answers to approved sources, make the model say it does not know when the lookup is weak, check extracted values with rules, and test against known-answer questions before launch and after every change. Low-confidence results go to a person. This cuts wrong answers sharply, but no one can promise zero.
For most business integrations, tens of dollars a month in model usage, not thousands (model prices checked October 2026). Costs climb with long documents, large models and agents that loop. We measure cost per task during testing, use smaller models where they pass the test set, and set hard monthly caps before launch.
Usually for ordinary reasons: no single job with a measurable right answer, no test set, so nobody knows how accurate it is, no cost limits, and no owner after launch when the provider changes a model. We start with one job and real examples, and report numbers before you commit to the build.
Ask any provider six things. Will you measure accuracy on my data before I pay for the build? What is the cost per task? Can I switch models later? Where does my data go? Who maintains it after launch? What happens when it is wrong? Clear answers in numbers are a good sign.
Usually, yes. Most integrations are a server-side API call plus the plumbing around it: queues, retries, validation and logging. We work inside your codebase, follow your conventions, and keep the keys on the server rather than in the browser.
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