PoC & Engineering
Technical Validation, PoCs & Contract Development
Turn an uncertain technical question into working evidence
When data is fragmented, RAG quality is uncertain, or database and data-platform choices need to be tested against real constraints, we define the decision, build a focused PoC, evaluate it, and prepare it for handoff. RAG implementation is an active engagement area.
Turn data transformation, analytics, RAG, database, search, and data-platform questions into working deliverables and evidence.
Key points
- Start with the customer problem and success criteria
- Deliver working artifacts together with evaluation results
- Select technology for the requirements, including established options
Customer Problems
There is something to validate, but not enough evidence to decide
Starting from a technology name often leaves the data, operating constraints, and evaluation method until later, so the PoC does not support a real decision.
- Data is spread across CSV, spreadsheets, JSON, APIs, and databases, blocking analysis or AI use
- A RAG system is needed, but ingestion, retrieval, answers, citations, updates, and evaluation have not been designed as one flow
- Database, search, and data-platform candidates exist, but there is no comparison using the team's own data and requirements
- A demo runs, but its code, configuration, and evaluation conditions are not reusable for production or handoff
The purpose of a PoC is not just to produce a demo. It is to add facts that support the next decision. We start from the customer problem and success criteria, then package the working implementation, evaluation results, and reproduction steps as one handoff-ready outcome.
Building Alopex DB provides the technical foundation for inspecting databases, search, and storage from the inside. An engagement does not require Alopex DB; the technology is selected for the existing environment and requirements.
A PoC that supports a go, no-go, or next-test decision
We align the problem, source data, constraints, and success criteria first. We then build the smallest useful implementation, record its results and limits, and leave artifacts that can be reused for production, further validation, or an alternative approach.
- Document assumptions, scope, and evaluation criteria
- Provide reproducible implementation and run instructions
- Separate what was verified, what was not, and what should happen next
Engagement Areas
Four ways to engage
Each area can stand alone, or form one engagement from data preparation through retrieval and RAG.
Data transformation & ETL
AvailablePrepare data for analysis, search, and AI
- Ingest and combine CSV, spreadsheets, JSON, APIs, and existing databases
- Align schemas and types, and handle missing, duplicate, and inconsistent values
- Deliver repeatable transformations with validation results
Data mining & analysis
AvailableTest a hypothesis against the data
- Explore distributions, trends, and relationships
- Extract anomalies and patterns and define evaluation methods
- Deliver reproducible analysis code and an explanation of results
RAG implementation
AvailableIngest documents, retrieve evidence, and answer with sources
- Document ingestion, chunking, embeddings, retrieval, and answer generation
- Compare full-text, vector, and hybrid retrieval
- Prepare citations, evaluation cases, and update flows
Database, search & data-platform PoCs
AvailableRun candidate technologies under the same conditions
- Shortlist database, search, and processing options from the requirements
- Validate behavior, performance, and operations with representative data
- Provide an adoption decision and a production-oriented architecture option
Deliverables
Artifacts built for decisions and handoff
The exact scope is agreed before work starts, together with how each deliverable will be checked.
Problem, requirements, and success criteria
A concise record of source data, constraints, scope, evaluation method, and completion conditions.
Working PoC or implementation
A reproducible validation app, data pipeline, retrieval or RAG flow, and its configuration.
Evaluation results
Verified results, comparison conditions, limitations, and remaining unknowns in a decision-ready form.
Code and run instructions
Source, configuration, dependencies, and reproduction steps organized for the next owner.
Recommended next steps
Prioritized options for production development, another validation cycle, or an alternative approach.
Process & Technology
Choose from the requirements and validate in short cycles
Alopex DB development provides first-hand experience in databases, search, and storage, but using Alopex DB is never a condition of the engagement. We consider the existing environment and established technologies, then choose the composition that fits the goal.
1. Review the problem and data
Discovery and scope
2. Agree on the validation plan
Deliverables and evaluation
3. Implement and review
Short validation cycles
4. Evaluate and hand off
Deliver decision-ready evidence
Technical Evidence
The perspective of a database engine developer
Alopex DB combines SQL, vector search, columnar storage, and embedded and server usage in one Rust engine. That experience supports validation across data shape, retrieval method, execution path, and operating constraints. Alopex DB remains one option; it is not used when another technology better fits the engagement.
“Start from the problem you have today. You will see quickly what this product removes.”
Takamine Akari
Product guide
“Open the repository and read the design rationale and the tests yourself.”
Asmati Tatsumi
Technical explanation
Public Resources
The latest documents, code, and community links in one place.
Contact
Tell us what you need to validate
For the first message, share what is blocked, the data you can use, the decision or deliverable you need, and your preferred timing as far as they are known. We can turn the remaining unknowns into a validation scope together. Price, duration, and team shape are proposed after reviewing the work.