SPECTRA builds applied AI systems for companies that manufacture, extract, sell and ship. Not a demo of what a model can do, but a governed digital process: domain rules, integration into your system of record, and a person who controls the exceptions.
12+ years in enterprise development, 2 years shipping AI
01 · Industries
One approach, different production circuits
Below are examples, not a closed list. Pick the one closest to you: we will show the processes where AI already delivers a verifiable result rather than just speeding up correspondence.
Engineering and production processes where the answer has to be verifiable, not merely plausible.
№
Scenario
What the AI takes on
01
Engineering selection and specs
Reads project documentation, structures the item list and runs selection against the manufacturer catalogues.
02
Quality control
Detects defects on the line and at intake from photo and video streams, and records deviations in the system.
03
Maintenance and tickets
Classifies requests, suggests the likely cause and the applicable procedure, and drafts the work order.
Item lists, price lists and customer flow — wherever manual reconciliation eats a working day.
№
Scenario
What the AI takes on
01
Supplier price lists
Normalises heterogeneous price lists and catalogues into a single product reference.
02
Assortment and ordering
Prepares stock and purchase recommendations from sales history and seasonality.
03
Customer service
A first-line AI agent answers from the knowledge base and hands complex cases to a person.
The document flow between supplier, warehouse and the system of record.
№
Scenario
What the AI takes on
01
Incoming orders
Extracts line items from email, PDF and Excel and moves them into the ERP without manual entry.
02
Document reconciliation
Matches waybills, invoices and contracts against the system of record and flags discrepancies.
03
Shipments and routes
Assembles trips and prioritises shipments within warehouse and transport constraints.
Procedures, field data and safety on sites where mistakes are expensive.
№
Scenario
What the AI takes on
01
Technical documentation
Answers from procedures, equipment datasheets and standards, with a reference to the source.
02
Shift reporting
Turns field notes and logbooks into structured reports for the control room.
03
Industrial safety
Monitors PPE compliance and presence in restricted zones from the video stream.
Citizen requests, the regulatory base and internal document flow, all requiring traceability.
№
Scenario
What the AI takes on
01
Citizen requests
Classifies, routes and drafts replies, naming the provisions that were applied.
02
Regulatory base
Finds and cross-references documents, showing revisions and related acts.
03
Document flow
Extracts data from incoming documents and tracks response deadlines.
02 · What we implement
Custom AI systems built around a process
Not an off-the-shelf box, not a chat window on top of a model. We design the system around one concrete process: its data, domain rules, roles, exceptions and integrations.
Document AI
Extracting and structuring data from PDFs, scans, emails and spreadsheets: specifications, waybills, contracts, equipment datasheets, procedures.
AI agents
Agents carry out routine operations inside your systems within defined boundaries and hand a person the cases where the data is not sufficient.
1C and ERP integration
The AI result lands in the system of record rather than in a chat: 1C, ERP, CRM, email, file storage and your existing document flow.
AI process audit
We take the current process apart and say plainly where AI will pay off and where the data and procedures need cleaning up first.
We start with one process
A pilot on a narrow section, a measurable result, and only then scaling to adjacent processes.
AI you can trust with part of an engineering process
AVZ Selector turns a project PDF specification into a ventilation equipment selection ready for engineering review. The AI recognises and structures the document, the domain core checks parameters against technical catalogues and runs the calculations, and the specialist gets an explainable result with exceptions flagged.
The AI reads the project. The engineering core runs the selection. The specialist controls the result.
The system can run fully autonomously. When a specification contains no items that call for engineering judgement, the whole cycle — from PDF upload to the finished table — runs without a human. Manual review kicks in selectively, only where the data is genuinely insufficient.
From a multi-page specification to a structured selection with models, components and review statuses. The screenshots use anonymised demo data.
The problem
Source data arrives as PDFs with inconsistent tables, notation from different manufacturers, incomplete characteristics and OCR errors. Copying rows into Excel is not enough: the specialist has to determine the purpose of each item, normalise dimensions and flow rates, verify the operating point against technical data, and spot the cases where an automatic answer would be unsafe.
The solution
A hybrid AI system: probabilistic models handle the understanding of difficult documents, while formalised domain logic makes the technical decision.
Notation and dimension normalisation, equipment classification, flow and pressure verification, valve actuator sizing, fan model selection and assembly of the related components. Selection relies on digitised catalogues rather than free generation by a language model.
Explainable result
Source parameters, the operating point check, candidate comparison and the ranking rule.
Human-in-the-loop
A doubtful item stays in the result with a specific reason for manual review.
Digitised catalogue
Sizes, operating ranges, pressure, rotation speeds and equipment power ratings.
What the system covers
Uploading the project PDF specification through the web app or a Telegram bot
Recognising and structuring the item list
Catalogue selection of fans, valve actuators and related components
Manual correction, comments and change history
Viewing the source PDF and exporting the finished table to XLSX
Regression quality checks on realistic specifications
Client testimonial
“I set the task myself: selecting equipment from a project specification took our engineers hours, and I could see that artificial intelligence should be doing this work. But an owner’s decision is not enough — without competent specialists a system like this does not appear.
SPECTRA got to grips with the domain at the level of catalogues, operating points and valve actuators, and took the product all the way to industrial use. In parallel the team worked on digitising the plant: modern technology became part of everyday office work rather than a separate project.”
Marat Bakkulov Founder and owner of AVZ LLP — Almaty Ventilation Plant.Chairman of the board of the Union of Manufacturing Industry of Kazakhstan.
04 · Portfolio
Machinery · AVZ
AVZ Selector
Engineering selection of ventilation equipment from a project PDF specification: Document AI, a catalogue core, explainability and manual control of exceptions.
Surveys and interviews in Telegram where the AI runs the open questions: it checks each answer against a completeness criterion, asks follow-ups within a set limit, and extracts a structured summary instead of raw text.
Takes inbound calls and holds the conversation by voice: consultation, tyre selection, service booking. It acts on the company’s rules and records the outcome in telephony, amoCRM and the calendar — with no waiting on the line.
A process map, an assessment of data quality and an honest list of what can be automated now and what has to wait until the data is in order.
02
Pilot
A working system on one narrow section and a set of regression tests on your real documents.
03
Rollout
Integration into 1C, ERP and document flow, roles and permissions, staff training, and a procedure for handling exceptions.
04
Support
Quality monitoring, retraining on feedback, extension to adjacent processes.
We do not promise an effect in percentages before the audit. The numbers appear after the pilot — on your data.
06 · Contact
Let us turn an expert process into an industrial AI system
If your specialists read documentation, check data against catalogues and standards, run routine calculations and assemble the result in spreadsheets by hand — that process can be made faster, more transparent and more scalable.
