Agentic AI
Agents that plan, call tools, and hand work back to people.
- Multi-agent orchestration
- Tool use & MCP
- Human-in-the-loop
- Guardrails
- Workflow automation
AI-native software engineering
AI systems that survive production.
We build agents, voice, and retrieval systems — and the full-stack software, data pipelines and infrastructure they run on.
Working across US, EU and GCC time zones · Response within 24 hours
algologix/agentsample trace812ms
- classify_intent"refund status"41ms
- retrievekb/policies · 4 chunks · 0.83118ms
- tool orders.lookup#A-19822204ms
- generategrounded · 2 citations449ms
- verifyguardrails · pass
p50 640msp95 1.2scost $0.004/run
- Production AI systems
- Voice agents at sub-second latency
- Python · TypeScript · AWS · GCP
- Evals and observability on every build
- You own the code, the models and the repo
Most AI projects die between the demo and the deploy. We build the other half — the evaluation suites, the guardrails, the latency and cost budgets, the observability and the boring infrastructure that turns a convincing prototype into a system your customers can rely on.
How we workCapabilities
What we build
The AI systems on top, and the product, data and infrastructure they run on. One team for both, because the second half is usually what decides whether the first one ships.
AI & agents
Generative AI products
LLM features that hold up under real inputs.
- Copilots
- Document intelligence
- Structured extraction
- Prompt & context engineering
- Fine-tuning
Conversational & voice AI
Chat and phone agents that hold a real conversation.
- STT
- TTS
- Realtime turn-taking
- Telephony & SIP
- Multilingual
- +1 more
RAG & knowledge systems
Answers grounded in your data, with citations.
- Ingestion pipelines
- Embeddings
- Hybrid & vector search
- Rerankers
- Graph RAG
- +1 more
Product & platform
Product engineering
Web and mobile products, built to be maintained.
- Next.js
- React Native
- Design systems
- SaaS & multi-tenant
- Billing
- +1 more
Backend & APIs
The services everything else depends on.
- FastAPI
- Node
- Go
- GraphQL
- WebSockets
- +2 more
Data engineering & analytics
Pipelines that make the AI worth having.
- ELT
- Streaming
- Warehouse & lakehouse
- dbt
- Data quality
- +1 more
Cloud, DevOps & platform
Ship it, run it, keep it cheap.
- AWS
- GCP
- Azure
- Kubernetes
- Serverless
- +4 more
Also:ML & computer visionMobile engineeringAI strategy & enablementDesign & product discoveryCreative & growth engineeringAll 13 services
Algologix Labs · our own products
We don't just build AI products. We run them.
Building and operating our own products keeps us honest about what happens after launch — the on-call, the cost curve, the model deprecations, the edge cases real users find in week two.
Relay
In buildLaunch AI voice and web agents on your own number.
- Visual agent builder — Design the conversation, the tools and the escalation paths. Versioned and testable, so a change is reviewable rather than a rebuild.
- Bring your own model — Claude, GPT, Gemini or open weights you host yourself. Model choice is a configuration, so a better one is a switch rather than a migration.
- Telephony and web — Inbound and outbound calling on your own numbers over SIP, or an embeddable widget running the same agent definition.
- Grounded knowledge base — Retrieval over your documents with citations, so an answer can be checked back to the source it came from.
- Tools, webhooks and integrations — Call your systems mid-conversation — CRM, calendar, helpdesk — and write the record before the call ends.
- Transcripts, evals and analytics — Every call transcribed and scored against your own evaluation set, with A/B testing between agent versions.
- Consent, redaction and audit — Recording consent in the flow, PII redaction before anything reaches a provider, configurable retention and a full audit trail.
What we are building toward
- Target:Sub-second turn-taking on a live call
- Target:Every answer grounded in a cited source, or no answer
- Target:A published cost per call before you commit to a volume
- Target:Barge-in handled as behaviour, not as an error state
- Target:Every call transcribed, scored and replayable against your eval set
Join the waitlist
Relay is in build. Leave your email and we'll tell you before it opens — that is the only thing we'll use it for.
Selected work
What we have built
Three projects, written at sector level. Each one names what was actually measured — because the interesting part of an AI project is never the demo, it is whether the numbers held once real users arrived.
- Healthcare · 2026
A knowledge assistant that shows its sources
A retrieval assistant over clinical policy and procedure documents, answering staff questions with span-level citations and refusing to answer when the documents do not support one.
- Answers carrying a verifiable citation
- —
- rag knowledge systems
- generative ai
- data engineering analytics
- Retail · 2026
Turning supplier documents into records
A document-extraction pipeline for a retail group, classifying and extracting structured records from supplier invoices and delivery notes arriving in every format a supplier felt like using.
- Documents extracted without human review
- —
- generative ai
- data engineering analytics
- backend apis
- Logistics · 2026
A voice agent that handles the overnight queue
An inbound voice agent for a freight operator, handling status enquiries and booking amendments outside staffed hours, with a clean handover into the morning queue.
- Overnight calls handled without a person
- —
- conversational voice ai
- agentic ai
- backend apis
Some of our work is under NDA. We are happy to walk through it on a call.
All workHow we work
Five stages, and what you get from each
The same process on every engagement, whether it runs six weeks or six months. Each stage ends in something you can hold — a document, a prototype, a running system — so you always know what you have bought.
- 01
Discover
Frame the outcome, audit data and systems, spike the risky part, size it.
You get
- Solution brief
- Architecture sketch
- Estimate
- Risk register
- 02
Design
Flows, interface, system and data architecture, agent and model design; prototype the riskiest path first.
You get
- Clickable prototype
- Technical design doc
- Evaluation plan
- 03
Engineer
One-to-two week slices, CI from day one, instrumented as it is built, demo every sprint.
You get
- Working software every sprint, not at the end
- 04
Evaluate & harden
Eval suites, red-teaming, guardrails, latency and cost budgets, load, accessibility and security review.
You get
- Eval report
- Performance budget
- Runbook
- 05
Launch & operate
Deploy, monitor, iterate on real usage, transfer knowledge.
You get
- Production system
- Handover pack
- Documented ops
Most agencies stop at 03. The gap between a demo that impresses and a system that survives is stage 04.
Principles
- Outcome first
- Thin vertical slices
- Evals over demos
- Cost and latency are requirements
- You own the IP, the repo and the models
- No black boxes
Technology
The stack we actually use
Chosen per problem, not per fashion. Here's what's in the toolbox.
A grounded agent, wired the way we wire them — retrieve, cite, answer, or decline.
"""A grounded support agent: retrieve, cite, or decline."""
from anthropic import Anthropic
client = Anthropic()
search_policies = {
"name": "search_policies",
"description": "Find policy passages answering a question.",
"input_schema": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"],
},
}
response = client.messages.create(
model="claude-opus-5",
max_tokens=1024,
system=(
"Answer only from retrieved policy, and cite the "
"passage id. If nothing matches, say so."
),
tools=[search_policies],
messages=[{"role": "user", "content": question}],
)- First-load JavaScript, gzipped
- 177,007 bytes
- CSS for the whole site, gzipped
- 19,834 bytes
- JavaScript animating this page
- 0 bytes
Those three numbers are this page, measured on 4 September 2026 — not a client's system. We publish our own before we ask you to trust anyone else's.
- Python
- TypeScript
- Go
- SQL
- Rust
- Swift
- Kotlin
- Anthropic Claude
- OpenAI GPT
- Google Gemini
- Meta Llama
- Mistral
- xAI Grok
- DeepSeek
- Qwen
- vLLM
- Ollama
- Text Generation Inference
- AWS Bedrock
- Amazon SageMaker
- Google Vertex AI
- Azure AI Foundry
- Together
- Fireworks
- Groq
- Replicate
- LangChain
- LangGraph
- LlamaIndex
- Model Context Protocol
- Anthropic Agent SDK
- OpenAI Agents SDK
- Vercel AI SDK
- Pydantic AI
- CrewAI
- AutoGen
- DSPy
- Temporal
- pgvector
- Pinecone
- Qdrant
- Weaviate
- Milvus
- Chroma
- Elasticsearch / OpenSearch
- Redis Vector
- Embeddings — OpenAI, Voyage, Cohere, BGE, E5
- Rerankers — Cohere Rerank, BGE-reranker
- Hybrid BM25 + dense
- GraphRAG
- Whisper
- Deepgram
- AssemblyAI
- ElevenLabs
- Cartesia
- PlayHT
- OpenAI Realtime
- LiveKit
- Pipecat
- Twilio
- Daily
- SIP / WebRTC
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- LightGBM
- Hugging Face Transformers
- PEFT / LoRA
- Ray
- OpenCV
- YOLO
- Detectron2
- Segment Anything
- ONNX Runtime
- TensorRT
- FastAPI
- Django
- Node.js
- NestJS
- Express
- Hono
- Go — Fiber, Chi
- GraphQL — Strawberry, Apollo
- gRPC
- WebSockets
- Server-Sent Events
- Celery
- BullMQ
- Kafka
- RabbitMQ
- Redis Streams
- Next.js
- React
- Tailwind CSS
- Vite
- Motion
- TanStack Query
- Zustand
- React Native
- Expo
- Flutter
- SwiftUI
- PostgreSQL
- MySQL
- MongoDB
- Redis
- DynamoDB
- ClickHouse
- Cassandra
- Supabase
- Neon
- PlanetScale
- Prisma
- Drizzle
- SQLAlchemy
- Airflow
- Dagster
- dbt
- Spark
- Flink
- Snowflake
- BigQuery
- Databricks
- Redshift
- Fivetran
- Airbyte
- Kafka Connect
- Iceberg
- AWS
- Google Cloud
- Azure
- Vercel
- Cloudflare
- Docker
- Kubernetes
- Terraform
- Pulumi
- GitHub Actions
- ArgoCD
- AWS Lambda
- Cloud Run
- Fargate
- ECS
- API Gateway
- CDN & edge
- LangSmith
- Langfuse
- Braintrust
- Ragas
- DeepEval
- Weights & Biases
- MLflow
- OpenTelemetry
- Datadog
- Grafana
- Prometheus
- Sentry
- OAuth 2.0 / OIDC
- Auth0
- Clerk
- HashiCorp Vault
- AWS KMS
- PII detection & redaction
- Llama Guard
- NeMo Guardrails
- Snyk / Dependabot
Engineering standards
What we commit to on every build
Not adjectives. Six things we do on every engagement, each one specific enough that you can hold us to it in a review.
Evals before demos
Every AI feature ships with a test set, a scoring method and a regression gate.
Latency and cost are requirements
We set a p95 and a cost-per-run target in Discover, and hold the build to it.
Observable by default
Tracing, structured logs and dashboards from the first sprint, not bolted on after an incident.
You own everything
Your repo, your cloud accounts, your model weights, your prompts. No lock-in, no rented black box.
Security is not a phase
Secrets management, least privilege, dependency scanning, PII handling and provider DPAs from day one.
Documented handover
Architecture docs, runbooks and a walkthrough. Your team can run it without us — that's the point.
How to start
Four ways to work with us
Most engagements begin with the smallest one that answers the open question. You can move between them, and we will tell you when a smaller one would do.
AI Sprint
2–3 weeks · Fixed scope, fixed price
Best for: Is this even feasible?
Request a quote for AI SprintWhat you get
- Working prototype of the riskiest path
- Evaluation baseline you can hold the next phase to
- Architecture and integration plan
- A costed build plan, so the next decision is an easy one
Product Build
6–16 weeks · Milestone-based
Best for: Idea → launched product
Request a quote for Product BuildWhat you get
- Discovery through launch, run as the five stages
- A demo every sprint against the real system
- Evals, guardrails and a performance budget before launch
- Documented handover — architecture docs, runbooks and a walkthrough
Dedicated Pod
Monthly, rolling · Monthly, rolling
Best for: An ongoing roadmap, embedded with your team
Request a quote for Dedicated PodWhat you get
- 2–5 engineers plus design and product
- Working in your tools, your repo and your ceremonies
- One point of contact and a weekly written update
- Scale the pod up or down at a month's notice
Managed & Support
Monthly retainer · Monthly retainer
Best for: It's live and it must stay live
Request a quote for Managed & SupportWhat you get
- Monitoring, alerting and on-call cover
- Model and dependency upgrades, including deprecations
- Iteration against real usage and the eval suite
- An agreed response window, written down
Fixed price where the scope is fixed. Time and materials where the discovery is real. We tell you which one applies before you sign, and we don't do change-order surprises.
In their words
What it is like to work with us
The part that matters is usually what happened after launch, so that is what we ask clients to write about.
They shipped a working voice agent in three weeks and then spent another two making it survive our actual call volume. The second part is the part nobody else offered.
Head of OperationsNorthwind Logistics We came in with a chatbot idea and left with a retrieval architecture that actually cites its sources. They talked us out of the more expensive option twice.
CTOKestrel Health Clear scope, weekly demos, no surprises on the invoice. The handover documentation was better than what our internal team writes.
FounderMeridian Labs They set a latency and cost budget in week one and held the build to it. Our per-conversation cost came in under the estimate.
VP ProductHalden Retail Group
Insights
Engineering in public
How we evaluate agents, the retrieval architecture we default to, and what an AI feature actually costs to run.
- Evaluation
Evaluating a voice agent before it goes live
The methodology, the metrics we score, and where the latency budget actually goes.
4 min read
- Retrieval
RAG that cites: the architecture we default to
The actual pipeline — chunking, hybrid retrieval, reranking and span-level attribution — with the trade-offs named.
4 min read
- Cost
What an AI feature costs to run
Token, inference and infrastructure maths, with a worked example you can put your own numbers into.
3 min read
Questions
Before you get in touch
The seven we are asked most often, answered properly rather than deflected to a call.
What kinds of projects do you take on?
AI systems that have to run in production — agents, copilots, retrieval over your own data, voice and chat agents, computer vision — and the product and platform engineering around them. If the work is an AI feature inside a real product, that is the middle of what we do. If it is a research question with no deployment at the end of it, we are the wrong team and we will say so on the first call.
Can you take something from idea to production, or only build to a spec?
Either. Most engagements start at Discover, where we frame the outcome, audit the data and spike the part most likely to fail — and end with a deployed system and a documented handover. If you already have a specification and a design, we can start at Engineer instead. What we will not do is build to a spec we think is wrong without telling you first.
Do you work with our existing engineering team?
Often, and it is usually the better arrangement. A Dedicated Pod embeds with your team in your repo, your tools and your ceremonies. On a Product Build we run our own cadence and review into yours. Either way your engineers are in the pull requests from the first sprint, because a handover at the end to people who have never seen the code is not a handover.
How do you handle our data — and do you train models on it?
No, we do not train on your data, and neither do the providers we configure — enterprise API terms exclude it, and we set that up rather than assume it. Data stays in your accounts and your region wherever the architecture allows. We apply PII detection and redaction before a payload leaves your boundary, keep provider DPAs on file, and write down exactly which data reaches which provider before anything is built.
Who owns the code, the prompts and the models?
You do. Your repository, your cloud accounts, your model weights, your prompts, your evaluation sets, your infrastructure as code. There is no runtime you have to keep renting from us to keep the system working, and no component you cannot read. If you end the engagement, everything keeps running.
How long does a typical project take, and how is it priced?
An AI Sprint is two to three weeks at a fixed scope and price. A Product Build is typically six to sixteen weeks, priced against milestones. Ongoing work is a monthly pod or a support retainer. We do not publish prices, because the honest number depends on scope we have not seen yet — but we tell you which pricing model applies before you sign, and we do not do change-order surprises.
How do you work with clients across time zones?
We work remotely with clients across US, EU and GCC time zones, and agree the overlap hours with you at the start rather than leaving them to chance. You get a demo every sprint, a written update every week, and a reply to anything you send within 24 hours. Nothing important depends on catching someone live.
Tell us what you're building.
A 30-minute call. We'll tell you honestly whether we're the right team, what we'd do first, and roughly what it costs.
contact@algologix.coResponse within 24 hours