AI that works on your data with a person on the risky steps

Assistants, document automation and agents connected to your real systems. Every answer is grounded, tested and costed, and anything risky goes to your team first.

Customer · chat · 14:02

"Can I change the delivery address on order 4471?"

Finance · inbox · 09:14

18 vendor PDFs and 4 customs declarations arrived overnight

Assistant · grounded

"Yes, it has not shipped yet. Send the new address and I will update it."

orders · 4471policy §3
CHECKS PASSED · 38s · $0.004
Document AI · extracted

20 of 22 documents posted to ERP automatically, 2 queued for one-click approval

confidence 0.97vat matched
ERP SYNC · 09:15

FOLLOW ONE REQUEST

What happens between a question and a safe answer

Six steps, most of them invisible to your customer. Tap through or press play.

A customer asks a question

"Can I change the delivery address on order 4471?" arrives in chat, email or inside your product.

EVERY CASE COSTED

The right model for the job, not the biggest one

$1.4kEstimated monthly model spend
Routed toMid model + retrieval
Latency p951.1s
Cost per case$0.004
Quality score94 / 100

Intent sorted by a small model, answer grounded in help centre and order data, cited. Large model never wakes up.

Routing ruleSimple and repeated goes small. Language and judgment go mid. Rare, high-stakes reasoning goes large, with a human sign-off.
Cost alarmAny task whose monthly spend crosses its payback line gets re-routed or cached before the invoice surprises anyone.
Quality gateEvery prompt or model change runs against your evaluation set first. Regressions never reach customers.

QUICK FIT CHECK

Is AI the right tool for this job?

Three honest questions. Sometimes the answer is a better form or a clear process, and we will say so.

  • Is the task repeated many times a week?

  • Does the answer live in your documents or data?

  • Can a mistake be caught before it reaches a customer?

Answer the three questions

We use the same test in our first call. It takes ten seconds.

Engineer testing an automated system at a workstation
Tested before it ships Every prompt and model change runs against your real cases first
Two-week sprint

Not sure where AI fits in your business?

We review your processes and data, rank the use cases by payback and build a working prototype of the best one.

96%Eval pass rate, last release
$0.004Blended cost per case
1.1sLatency p95, support flow
Quality score trend across weekly releases
  • EVAL #2211passed · citation checksupport
  • EVAL #2212flagged · tone driftprompt patch queued
  • EVAL #2213passed · PII scandocs
  • EVAL #2214passed · tool limitsagent
  • EVAL #2215flagged · stale priceindex refresh

WHAT WE BUILD

Four places AI usually pays back first

01

Customer assistants

Answers grounded in your help centre, orders and policies.

A chat or email assistant that resolves the common questions and hands over the rest with a summary. It cites its sources, respects your tone and never invents a policy.

RAGHelp centre syncHandover to humansMultilingual
Plan a customer assistant ↗
02

Document and inbox automation

Invoices, contracts, forms and emails turned into clean data.

Extract fields, classify documents, route emails and fill your systems. A confidence score decides what goes straight through and what a person checks.

ExtractionClassificationConfidence routingERP / CRM write-back
Automate my documents ↗
03

Copilots inside your product

Summaries, drafts and search that your users actually open.

We add AI features where users already work: summarise a record, draft a reply, search across everything. Usage, cost and quality are measured per feature.

In-product AISemantic searchUsage meteringFeature flags
Add AI to my product ↗
04

Workflow agents

Multi-step tasks across your tools, with clear limits.

Agents that research, update records and trigger workflows across your CRM, helpdesk or internal tools. Every action is permissioned, logged and reversible.

Tool callingPermissionsAudit logHuman approval
Scope a workflow agent ↗

THE WHOLE ROUTE ON ONE DRAWING

How requests, models and your team connect

AI integration drawing: requests routed by a classifier to small, mid or large models, grounded on company data, checked by a guardrail and a human reviewer, then written back to business systems ai.integration.blueprint route · ground · check · act CHAT EMAIL · INBOX IN-PRODUCT ROUTER intent · risk · cost SMALLsort · extract<200 ms MIDanswer · draft~1 s LARGEreason · planonly when needed YOUR DATA DOCS CRM ORDERS CHECKS grounded · cited no personal data leak refund > $200 YOUR TEAM approves risky steps APPROVE WRITE BACK · LOG · SCORE Helpdesk, CRM and ERP updated QUALITY 94 $0.004 / CASE

WHERE IT LANDS

AI work your teams feel in week one

Support agent working alongside an AI assistant
Customer assistant

Answers that cite your own docs

Grounded replies in 12 languages with handover summaries your team can trust.

Plan an assistant
Contracts and invoices being parsed automatically
Document automation

Invoices and contracts into clean data

Extraction with confidence routing: straight-through when sure, human when not.

Automate documents
Product team reviewing an in-app AI copilot
In-product copilot

AI features your users open twice

Summaries, drafts and semantic search inside your product, metered per feature.

Add AI to product
Operations dashboard watching AI workflows
Workflow agents

Agents with permissions and undo

Multi-step work across CRM and helpdesk, every action logged and reversible.

Scope an agent

FOR SOFTWARE & SAAS TEAMS · PRODUCT AI

Add AI to the product your customers already use

Already shipping a B2B SaaS platform, financial application, ERP or document system? Our applied AI and ML engineers (led by M.Sc. AI & AWS Certified ML leadership) build production-ready AI features directly inside your existing product without distracting your core roadmap team.

In-App Copilot & Search

Embedded SaaS Copilot & RAG

Let your users ask questions, generate reports or trigger actions inside your software using tenant-isolated RAG and permissioned tool calls.

3 to 5 weeks
  • Strict per-tenant data isolation & RBAC security
  • Semantic search, chat UI & plain-English reporting
  • Prompt caching & model routing to protect margins
  • Evaluation suite & hallucination guardrails
Discuss an AI Feature ↗
Document & Vision AI

OCR, Document & Vision Pipelines

For fintech, legal, healthcare and logistics platforms that process invoices, contracts, IDs, signatures or visual inspections at scale.

4 to 8 weeks
  • Custom OCR, table extraction & document classification
  • Signature verification, fraud & defect detection (YOLO/CNN)
  • Fine-tuned domain models (PEFT/LoRA) on AWS/private cloud
  • Confidence scoring with human-in-the-loop review queues
Get an AI Assessment ↗
Model Ops & Cost Cut

LLM Evaluation, MLOps & Cost Audit

Already launched an AI feature that is too slow, too expensive or failing edge cases? We optimize latency, accuracy and token spend.

2 to 3 weeks
  • 30–50% inference cost reduction via smart model routing
  • Automated regression evals before every prompt/model change
  • Encrypted vector storage & SOC 2 / GDPR compliance checks
  • Clean API handoff to your internal engineering team
Optimize Our AI Stack ↗

WAYS TO START

Small first step, measured next step

Start here

AI Fit Sprint

Find the two or three AI use cases worth building, with numbers.

2 weeks
  • Process and data review
  • Use case ranking by payback
  • Working prototype of the top one
  • Cost and risk estimate
Book an AI Fit Sprint ↗
Most asked

Production Assistant

One assistant or automation, live, measured and safe.

6 to 10 weeks
  • Grounded on your data
  • Evaluation set and quality gate
  • Human review for risky cases
  • Cost and quality dashboard
Plan my assistant ↗
Ongoing

AI Operations

Keep models, prompts and costs in check as usage grows.

Monthly
  • Model and prompt updates
  • Regression tests on every change
  • Cost routing and reports
  • New use cases each quarter
Talk about AI operations ↗

Adjacent disciplines

Route · Ground · Review

Ready to put AI on a real processwith numbers you can check?

Tell us the process or the pain. We reply with the use case worth starting with, a plan and a fixed scope.