Supplier documentation
Specifications nobody can find
Thousands of supplier PDFs sit in a folder, holding the voltages and compatibility notes nobody can find. Staff ask in their own words and get the paragraph, rather than a list of files to open.
Vector database development
The model is not the hard part. Finding the right page is.
An assistant that answers well sits on documents somebody prepared. Pointing a model at a shared drive gets you confident answers drawn from whatever happened to match.
- Runs inside your own database
- Answers cite the passage they came from
Your documentsPDFs and manuals
Your transcriptsTickets and calls
CleanedWhatever shape it arrived in
Split by meaningNot by page
EmbeddedComparable to a question
In your databasepgvector, beside the records
Kept currentThe pipeline keeps running
What AI data engineering is
AI data engineering turns the material a business already has into something a model can search by meaning rather than by keyword. Documents are split into passages, and each passage becomes a vector that can be compared against a question. The pipeline that keeps doing this as new documents arrive is most of the work. It decides whether answers stay good.
The model was never the bottleneck
Teams connect a model to their files and get answers that sound right and cite nothing. The problem is almost never the model. It is that nothing has decided which passage should have been retrieved before the question was asked.
- 01
Keyword search misses the question
Somebody asks about a refund window. The document says returns period. A keyword index finds neither for the other.
- 02
The documents were never prepared
A PDF split at page boundaries cuts sentences in half. What gets retrieved is a fragment that answers nothing.
- 03
It goes stale the week after launch
The index is built once and the documents keep changing. Answers drift away from the truth quietly.
The architecture of an AI ready backend
- 01
Pipelines that keep running
Material is pulled from wherever it lives, then cleaned and loaded on a schedule you set. New documents reach the index unprompted.
- 02
Chunked where the meaning breaks
Documents are split on their own structure. A passage that ends mid argument retrieves badly.
- 03
Vectors in the database you run
pgvector puts vector search inside your existing PostgreSQL. One database to back up.
How the first three weeks run
- Week 1
01We look at what you actually hold
Two days on where the material lives. Document quality decides more here than model choice does.
- End of week 2
02You get a costed plan
The plan names the first sources and the cost. You decide whether it goes ahead.
- From week 3
03One source, measured before the next
The first corpus goes in. Retrieval is tested against questions you supply. More sources before that is how these go wrong.
A SQL query against a vector search
They are not rivals and they answer different questions. Most systems end up needing both.
| Measure | SQL search | Vector search |
|---|---|---|
| What it matches on | The characters you typed | What the passage means |
| What it holds | Rows and columns | Text, and what it is about |
| A question in other words | Finds nothing | Finds the passage anyway |
| What it is good at | Totals, filters and exact lookups | Retrieval for a model to answer from |
| Where it lives | Your database | Also your database, with pgvector |
Vector search in real workflows
Three sets of documents a business already has. Each becomes answerable rather than merely stored.
Financial records
History a model can reason over
Receipts and ledger entries are embedded beside the documents that explain them, in one store. A new transaction gets classified from similar ones.
Recorded calls
Transcripts that become searchable
Audio is transcribed, chunked and embedded into a store that sits apart from everything else. Past conversations become searchable by subject.
Where your material sits
This work touches everything a company has written down, so where it ends up matters more than usual. It is decided before the first pipeline runs.
- In your own database
- pgvector keeps the vectors beside the records they came from.
- Retrieval is auditable
- You can see which passages were returned for any question.
- Nothing is kept by a model
- Embedding endpoints run with retention off.
What the pipeline is built from
Standard components, chosen so nothing needs replacing later. Nothing is a requirement, and the work goes ahead against whatever your systems were built on.
- Where the vectors live
- pgvectorVector search inside PostgreSQLPostgreSQLThe database you already runRedisCaching the frequent lookups
- Turning documents into vectors
- EmbeddingsThe representation itselfClaudeReading and structuring documentsWhisperAudio into text first
- Keeping it current
- PythonThe pipeline and its testsn8nWhat runs when a document landsDockerSame everywhere it runs
What people sayNot our words.
Not our words.
Theirs.
Every quote here is a real Trustpilot review. We did choose which ones to show you. The score beside them is the part we do not control, and it counts all 29 reviews.
Trustpilot4.4out of 5Excellent29 reviews · checked 19 August 2026Read all of them
These reviews are for Appkodes, our software product division.
A Joysale client on the product and the service
He runs a marketplace built on Joysale, our Letgo style product. The clip is his own account of working with us.
A Fantacy client on the build
Fantacy is our Amazon style retail product. He goes through what was built and how the work ran.
An Airfinch client, filmed after his written review
Airfinch is our Airbnb style rentals product. He had already left the same review on GoodFirms before recording this.
A second Joysale client on the same product
Another marketplace running on Joysale. Worth watching beside the first, since the two bought the same thing.
Trustpilot
I've worked with Appkodes for 7 years on 4 different projects. We constantly require support or the implementation of new features, and we have the guarantee that the quality of their work remains the same throughout this time.
Juan VásquezSep 2025Trustpilot
Appkodes exceeded all of our expectations! From the very first contact, the team demonstrated a high level of professionalism, technical expertise, and commitment to quality.
Global SoftwinMay 2025Trustpilot
Appkodes team helped me to launch my healthcare application very quickly. Their software was very close to my requirements and adding some extra features made my project easy.
WassimApr 2025Trustpilot
I so much love your services and I will continue to patronize your company.
Kolawole Alaba JohnsonApr 2025Trustpilot
I worked with AppKodes for a website and mobile app development project, and overall, I'm very satisfied with the results. Their team was responsive and flexible throughout the process, and they delivered a product that met my expectations both in design and functionality.
Яша ФирузApr 2025Trustpilot
It was a good experience working with the team. They understood my ideas clearly and built everything as expected. The team was supportive, quick to respond, and helped me whenever I needed changes. Thank you for your hard work and support!
Prem SharmaApr 2025Trustpilot
I have got a mobile app project going on successfully with the team. Their Support is good. turn around time for any requirement is great. Every detail of my app is meticulously designed. THANK YOU APPKODES.
Fast Fiber NetworksApr 2025Trustpilot
Appkodes is a leader in developing high-quality applications and websites. It was a pleasure working with them, and this certainly won’t be our last collaboration. My experience was exceptional, they developed an outstanding app and website, with smooth and refined interactions.
BaderApr 2025Trustpilot
Overall very good experience. I have been availing services for past 3 years. They are available for discussions and resolving issues whenever we faced any. Mr. Saravana has been looking after our project and I'm very much happy with his timely response. I would definetely recommend.
Vannala RajuApr 2025Trustpilot
Initially, I was hesitant to deal with them, believing their customer service would be poor. However, I was surprised. They act with great responsibility and professional efficiency. My regards to them.
Ahmed NhariApr 2024Trustpilot
You have been supporting me very quickly in every matter, especially in the last 2 months, and this makes me very happy.
Deniz SeçerJan 2024Trustpilot
Very professional. Our project was quite complex and they covered all the aspects. Appkodes did an amazing and professional job developing and creating our Apple and Android apps. I was positively impressed with the communication you can absolutely trust on what they say.
Anu JosephJul 2023Trustpilot
Mani and Saravanan of the Appkodes team are amazing, they have done the best to create and support my project! I give them 10/10 stars for their efforts and work!
Joely CineasJul 2023Trustpilot
It was really great, they are there for me whenever I had a problem or to fix something. Thank you so much Ameer
GomezApr 2023Trustpilot
I've been working with Appkodes for almost a year and i can recommend them to work with as they are so much friendly and professional and you can clearly see it once you start your project right away. They are intact and they are transparent with their communication.
JohnFeb 2023Trustpilot
I have to be honest, sometimes it's hard to find a company or someone abroad to do your project. Not only might you waste your time and money, there is this thing called trust. In business you must trust the person you are dealing with.
Zack GizawDec 202216 of 29 reviews, plus 4 videos
Businesses we have built for
Frequently asked questions
Can you add vector search to our existing database?
Usually, and it is the first thing worth trying. pgvector adds vector search to a PostgreSQL database you already run, so there is one system to back up and your material does not move. A separate vector store earns its keep at volumes most businesses will never reach in practice.
Which embedding models do you use?
It depends on the material and where it may go. Hosted embedding endpoints are the usual choice. Where documents cannot leave your network, an open weight model produces the vectors on your own hardware instead.
Will the AI still make things up?
Retrieval makes it much less likely. It does not make it impossible. Good data engineering buys you two things. The model answers from passages it was handed rather than from memory, and you can see which ones. Where nothing relevant comes back, saying so is correct.
How do you handle documents that keep changing?
The pipeline runs on a schedule rather than once. New and changed material is picked up, re-chunked and re-embedded, so the index is current as of the last run. How often it runs is a decision about cost.
What is RAG data architecture?
It is the half of a RAG system that is not the model itself, and it does most of the work. Storage, chunking and retrieval all sit here. They decide answer quality far more than the model does.
Do you handle unstructured data vectorization for audio and images?
Yes. Audio is transcribed first and then treated as text. Images are handled by a vision model that describes them, so what gets embedded is a description somebody could also read.
Next stepLet AI do the repetitive
Let AI do the repetitive
half of the job.
Data entry, answering the same tickets, chasing numbers between systems. We automate the parts that repeat. Your team keeps the parts that need judgement.
