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AI & ML SERVICES

AI that ships not just slides.

Most AI projects stall between the demo and production. We build the engineering around the model — the sandboxing, the routing, the integrations, the guardrails — so your team can actually trust it with real customers, real data, and real budgets.

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See what we've built
Years building production data & AI systems
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Clients across SaaS, fintech, retail and ops
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Engineers, ML specialists and analysts on staff
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What we build

Five things we're known for, in production.

These aren’t demos or proof-of-concepts. Each capability below is something we’ve shipped to running businesses, with measurable outcomes attached.

AI / Safety

Sandboxed AI agents that can't break things

When an AI agent writes code or executes actions on your data, one wrong step can be expensive. We run agents inside isolated E2B sandboxes — every action is contained, reversible, and logged. The blast radius of a mistake stays small enough to ignore.
Use case → Agents that touch databases, files, or APIs

AI / Reliability

Multi-model routing that pays for itself
One model rarely fits every task. We orchestrate across providers — Anthropic, OpenAI, open-weights — sending simple jobs to cheap fast models and reserving frontier models for what actually needs them. The result: lower bills, higher uptime, and a fallback when any one provider goes down.
Outcome → Typically 40-70% cost reduction without quality loss

AI / Knowledge

Slack agents that hold your team's tribal knowledge

The institutional knowledge in your senior engineers' heads — naming conventions, why-we-did-it-this-way, cost-saving patterns, review checklists — gets lost in DMs and old threads. We build Slack agents that absorb this knowledge and answer the same questions tirelessly, so your seniors can stop being a help desk.

Outcome → Senior time freed, juniors unblocked faster

AI / Automation

Process automation with magnetic workflows

The repetitive coordination work no one wants to do — pulling Jira tickets, posting standup summaries, tagging customer signals, syncing systems — gets automated with n8n and AI-assisted routing. Errors come back, errors drop, and the work happens whether anyone's online or not.

Use case → Ops, engineering, RevOps, customer success

AI / Integration

Custom MCP servers — your tools, exposed cleanly to any AI

The Model Context Protocol (MCP) is becoming the standard way AI assistants talk to internal systems. We build custom MCP servers that expose your tools to Claude, ChatGPT, or any compliant agent — securely, with the right permissions, ready for whatever model your team uses next.

Outcome → Future-proofed AI integrations that aren't locked to one vendor

IN THE WILD

What this looks like in your team's Slack.

Two recent examples of what an AI deployment looks like when it’s built right — invisible, dependable, and quietly compounding value every week.

ENGINEERING KNOWLEDGE AGENT

A Slack bot that answers 50+ deep technical questions a week

A senior engineering team was being interrupted constantly with the same questions: what’s the right data pattern for this use case, what are we thinking about this PR. We built a Slack agent that picked up the team’s tribal knowledge — code conventions, architecture decisions, cost-saving patterns, review heuristics — and started answering directly in-thread.

Slack

Rag

Multi model routing

GitHub MCP

50 +

deep technical questions answered every week

3 areas

data patterns, cost saving, and PR review

STANDUP AUTOMATION AGENT

No more "what did you do yesterday" meetings

The team’s daily standup was eating a collective hour every morning — and the summary nobody read after. We built an automation that pulls each engineer’s ticket activity and commits, drafts an individualised standup, posts the team summary to Slack, and flags blockers automatically. The meeting still happens when needed — but the routine version doesn’t.

n8n

Jira API

GitHub API

Slack

LLM summarisation

Daily

automated standup summaries, no human prompt needed

3 systems

integrated end-to-end, one agentic flow

How we engage

Built like engineering work, not like consulting decks.

We work in short cycles. You see something running in your environment in weeks, not quarters. Here’s the rhythm.

01 / week 1

Scoping & problem framing

We sit with your team, look at the actual workflow, and pick the highest-leverage point to apply AI. No generic strategy deck. Just one clear problem to solve first.
02 / WEEKS 2–4

First working build

A live prototype in your stack, with real data, that your team can poke at. Wrong assumptions get caught here, while changes are cheap.
03 / WEEKS 5–8

Hardening for production

Sandboxing, observability, evals, observability, cost guardrails. The unglamorous engineering that decides whether AI lasts past month two.
04 / ONGOING

Handover or support

Documentation your team can actually read, plus an optional support retainer. If you want us out by month three, that’s fine — we plan for it from day one.
THE STACK WE WORK WITH

Production-grade tools, chosen on merit.

No vendor lock-in. We pick what fits your existing infrastructure and your team’s skill profile.

MODELS & APIS

Anthropic Claude

OpenAI GPT

Gemini

Llama (open-weights)

Mistral
Together / Fireworks

AGENT & INFRA

E2B sandboxes

MCP servers

LangGraph
n8n
LiteLLM
Pydantic AI

DATA & VECTOR

BigQuery

Snowflake

Postgres / pgvector
Pinecone
Qdrant
dbt

INTEGRATIONS

Slack
Jira
GitHub
Notion
HubSpot
Salesforce
Linear
WHY TEAMS PICK US

Eight years of data work behind every AI decision.

We didn’t start as an AI shop chasing the trend. We’ve been doing data engineering and analytics for 100+ clients since 2017 — which means we know what production looks like, and we know where AI is genuinely the answer versus where it’s just expensive theatre.

Data foundations come first

Most teams have opinions. High-performing teams have data. A/B testing lets you replace gut-feel decisions with evidence, testing changes to your product, messaging, or UX against real users before committing.

Cost & reliability obsessed

We build with the monthly invoice in mind. Multi-model routing, caching, fallbacks — the stuff that decides whether AI is sustainable past year one.

No lock-in by design

Built on open protocols like MCP, on infrastructure you own. If you fire us tomorrow, your systems keep running. That’s the point.

Senior team, not handed off

The engineers in the discovery call are the ones who write the code. No two-tier model where the seniors leave after kickoff.

Short cycles, visible work

We are running output in weeks. Course correction happens early when it’s still cheap, instead of at the end when it isn’t.

We say no when AI isn't the answer

Sometimes the right answer is SQL query, a dashboard, or a script. We’ll tell you. We’d rather give back a contract than ship AI theatre.
QUESTIONS WORTH ASKING

Before you commit, here's what teams want to know.

What does it cost to build something like this?

Kaliper works in short cycles (weeks, not quarters), so costs are scoped per engagement rather than a large upfront contract. A typical project starts with a Week 1 scoping phase, followed by a 2–4 week working prototype, then hardening for production in weeks 5–8. Ongoing support retainers are optional. Pricing varies by complexity, but their model is built around being cost-conscious — multi-model routing alone typically cuts AI inference costs by 40–70%.

Two primary mechanisms:

  • Sandboxed agents — All agents run inside isolated E2B sandboxes. Every action is contained, reversible, and logged. The blast radius of any mistake stays small.
  • Hardening phase — Weeks 5–8 of every build are dedicated to evals, observability, cost guardrails, and safety engineering. This is the unglamorous work that determines whether AI survives past month two.

Kaliper explicitly builds on infrastructure you own, using open protocols like MCP. They are no lock-in by design — if you stop working with them, your systems keep running. They use production-grade providers (Anthropic, OpenAI, open-weights) where data handling is governed by enterprise API agreements, not training pipelines. You should confirm specifics per model provider during your discovery call.

No. Kaliper’s handover phase includes documentation your team can actually read, and they offer an optional ongoing support retainer. Their explicit philosophy is: “If you want us out by month three, that’s fine — we plan for it from day one.” They build with open protocols so you’re never dependent on them or any single vendor.

Using a single model means paying frontier prices for every task, even simple ones. Kaliper orchestrates across multiple providers — Anthropic, OpenAI, open-weights like Llama, Mistral — routing:

  • Simple tasks → cheap, fast models
  • Complex tasks → frontier models

The result is typically 40–70% cost reduction without quality loss, plus higher uptime since there’s an automatic fallback if any one provider goes down.

MCP (Model Context Protocol) is becoming the standard way AI assistants talk to internal systems — databases, CRMs, ticketing tools, proprietary APIs. Kaliper builds custom MCP servers that expose your internal tools to any compliant AI (Claude, ChatGPT, etc.) securely and with the right permissions. Why it matters: your AI integrations won’t be locked to one vendor or one model — whatever model is best next year, it’ll already be able to talk to your systems.

Pretty fast. Their engagement rhythm is:

  • Week 1 — Scoping & problem framing
  • Weeks 2–4 — First working prototype in your stack, with real data

So you can expect something live and testable within 3–4 weeks of kickoff.

This is exactly what their architecture is designed for. Because they build on open protocols (MCP) and use multi-model routing infrastructure, swapping out an underlying model is a routing change — not a rebuild. No lock-in by design means your systems keep running regardless of which model you use next.

Yes — this is actually a core differentiator. Kaliper has been doing data engineering and analytics since 2017, well before the AI wave. Their principle is “data foundations come first” — no AI works without clean data underneath. They can build the warehouse, the pipelines, and the AI model within the same engagement, so you’re not stitching together separate vendors.

Let's see if there's actually something here.

A 30-minute call. We’ll look at where you think AI fits, where it probably doesn’t, and what a first useful build might look like. No deck, no proposal under the door — just a working conversation.

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