Internal knowledge copilots
Search and Q&A over policies, codebases, tickets and runbooks — scoped to who's allowed to see what.
ENTERPRISE AI PRODUCT ENGINEERING
AI products built to explain themselves.
Algonix designs and ships full AI-native SaaS products, not chat widgets bolted onto a model. We build the retrieval layer, the data pipelines, the multi-tenant architecture, and the eval harness behind it — then run it in production at the scale a real enterprise customer base demands.
- retrieval-augmented generation
- multi-tenant SaaS architecture
- production ML/LLM ops
- grounded & reviewable
Data Layerone query
Chunks + embeddings
vector search index (HNSW)
Lexical index
keyword / BM25 ranking
Application data
permissions, metadata, joins
— one query, one access-control boundary —
01
Retrieval-first, not prompt-first
We start from what the model is allowed to see. Getting retrieval right does more for quality than prompt tweaking ever will.
02
Product engineering, not a demo
Auth, billing, multi-tenancy, admin tooling, background jobs — the unglamorous 80% that turns a model call into a product customers pay for.
03
Evaluated, not eyeballed
Retrieval quality and answer groundedness are measured against test sets before launch and monitored continuously after.
04
Built for the second year
Sane data models, straightforward re-indexing, and infrastructure your own team can operate without us standing over it.
What we build
Full products, not chat windows.
A chatbot is one surface. We build the product underneath it — the workflows AI actually needs to plug into to be useful at enterprise scale.
AI features inside existing SaaS
Summarization, drafting and search woven into a product's existing screens and permissions model — not a separate app.
Document & workflow automation
Intake, extraction, classification and routing pipelines that replace manual review queues at volume.
Multi-step, tool-using agents
Agents that call your APIs and internal tools with the same access-control and audit trail as any other service.
Search & discovery layers
Hybrid semantic and keyword search as core product infrastructure, built to be a dependency other features can rely on.
The SaaS platform underneath
Multi-tenancy, usage metering, admin consoles and integrations — the product surface around the model, built to scale.
How it's built
One retrieval layer, tightly coupled to your data.
Documents, embeddings, and your existing application data stay close together — so retrieval can be filtered and joined against permissions and business logic inside the same query, instead of stitched together after the fact. Built on PostgreSQL and pgvector.
Application · RAG service (embed → retrieve → generate)
Query
Dense — semantic / vector similarity
Lexical — keyword / BM25
RRF fusion
Top-k context
score(d) = Σ 1 / (k + rank(d))
Reciprocal Rank Fusion
Indexing tuned to the workload
We choose index type and parameters for your recall/latency trade-off and update cadence — not a one-size-fits-all default.
Chunking as a design decision
How documents are split and overlapped shapes retrieval quality as much as the model does — a first-class part of the architecture.
Production, not prototype
A demo is a weekend. A production system is the job.
We run what we build. That means autoscaling under real traffic, cost and latency budgets held to, and a rollback plan for the day a model provider changes behavior under you.
Build→
Evaluate→
Deploy→
Monitor→
Iterate
versioned · rolled out graduallyObservability & tracing
Every request traced end to end — retrieval, prompt, model call, cost — so a bad answer is debuggable, not mysterious.
Latency & cost budgets
Response-time and per-request cost targets set upfront and engineered for, not discovered after the invoice arrives.
Versioned prompts & indexes
Prompt, index and model changes are versioned and rolled out gradually, with a fast path back to the last known-good state.
Load-tested for real usage
Concurrency and throughput tested against expected enterprise traffic, not a single-user demo script.
Model-provider independence
Architected so a model swap or provider outage is a config change, not a rebuild.
On-call & incident response
A real runbook for degraded retrieval, provider incidents, or a cost spike — not a hope that nothing breaks.
How we work together
From your data to a product your team can operate.
- 1Phase 1
Discovery & data audit
What data exists, who can see what, and what “correct” looks like for this product.
- 2Phase 2
Architecture & prototype
Retrieval design, data model, and a working prototype validated against real evaluation sets.
- 3Phase 3
Production build
Multi-tenancy, auth, admin tooling, observability — the product engineering that makes it enterprise-ready.
- 4Phase 4
Deploy & operate
Launch, monitor, and hand over a system your own team can run — or we keep operating it with you.
Governance
AI systems your team can stand behind.
“It works” isn't the bar we build to — “we can show what it did, and why” is. Access control, logging and evaluation are part of the architecture from day one, not a review step bolted on before launch.
Access control at the query level
Retrieval is filtered against permissions and tenancy inside the same query — access rules can't be bypassed downstream.
Checkable groundedness
Cited sources are verified programmatically as a subset of what was retrieved — a log of what the model was and wasn't shown.
Your infrastructure, your control
We build in your environment where required, so data handling and access review stay entirely within your existing policies.
Evaluation before and after launch
Retrieval and answer quality are measured against test sets pre-launch, then monitored in production.
From our engineering team
Building RAG on PostgreSQL and pgvector: a practitioner's architecture guide.
The reference architecture, indexing trade-offs, hybrid-retrieval pattern and decision framework behind how we build — written up in full, with sources.
Read the paper (PDF)- §1 Introduction
- §2 Background
- §3 Single-engine case
- §4 Reference architecture
- §5 Vector indexing & quantization
- §6 Hybrid retrieval / RRF
- §7 Generation layer
- §8 Scaling & operations
- §9–10 Decision framework
Get in touch
Tell us what you're building, and we'll show you how we'd ground it.
A short call with our engineering team — no deck, just your data and your product.