Technology
Technology & Platforms
Service
Artificial Intelligence
AI that handles the busywork and leaves the judgment to you
AI, done right, starts with a hard question answered honestly: is this actually worth building, or does it just sound good in a meeting?
Teams come to this with a feature idea, a workflow they want to automate, or a system built by someone else that isn’t delivering. In every case, it’s hard to know the answer without building something. In most cases, the cheapest way to find out is a small test on your own data. What matters is the size of that something: a small, time-limited prototype made to find out, or six months of full production work that finds out by accident.
From there, we choose the approach (an autonomous agent, RAG over your documents, fine-tuning, or a simple automation) against a clear quality, latency and cost target, not by how impressive it looks in a demo. We test it on real data and real usage, not on clean examples, and build an evaluation set and guardrails from day one, not after something goes wrong. Wherever a wrong answer is expensive, a person stays in the loop.
Before any of that, we check whether your data is good enough and where it is allowed to go. After launch, we keep watching quality and cost, because models and data change.
Artificial Intelligence
Services
AI Feasibility & Proof of Concept
AI Feasibility & Proof of Concept
A focused, time-boxed build to find out whether an AI approach can actually hit the accuracy and cost target needed, before committing to a full build. Most AI budgets get wasted after the fact — months of development before anyone discovers the target was never realistic, or that a simpler, non-AI system would have solved the problem just as well. A clear answer either way in weeks, before serious money is spent.
AI Agents & Workflow Automation
AI Agents & Workflow Automation
Agents that don't just answer a question but carry out a multi-step task across existing systems: looking something up, updating a record, triggering the next step in a process. A chatbot that only answers still leaves someone to go do the actual work — the difference here is the agent does it, with the tool access and guardrails to be trusted with a real action, not just a conversation.
AI-Powered Search & Retrieval (RAG)
AI-Powered Search & Retrieval (RAG)
Semantic search across internal documentation, support tickets, or a knowledge base, grounded in the company's own content instead of a generic answer. Most internal search fails not because the answer doesn't exist somewhere, but because keyword search can't match a question phrased differently than the document that already answers it. Built to retrieve based on meaning, and to show where the answer actually came from.
Document Intelligence & Data Extraction
Document Intelligence & Data Extraction
Pulling structured data out of invoices, contracts, scanned forms, and other documents that don't follow a consistent template. Most manual data entry exists because a script built for one document layout breaks the moment it shifts slightly — a new vendor, a slightly different form. Built to handle that variation, feeding directly into the systems that already run the business instead of a spreadsheet someone re-keys by hand. everyone assumes it still works the way it did on day one — and without an evaluation set in place, nobody can tell if a fix actually helped until it's already back in front of users.
Sales & Lead Enrichment Automation
Sales & Lead Enrichment Automation
Automatically pulling context on an inbound lead — company, role, intent signals — before a sales rep ever looks at it. Most lead research happens manually, in the five minutes before a call that was booked an hour ago, or doesn't happen at all and the rep walks in cold. Built to have that context ready the moment a lead comes in, not researched on the fly.
Customer Support & Ticket Automation
Customer Support & Ticket Automation
Resolving common tickets automatically and routing the rest to a human with full context already attached, instead of a customer repeating themselves after a bot gives up. Support automation usually gets judged on deflection rate, which rewards handling easy tickets and frustrating the hard ones — the more useful measure is whether the handoff, when it happens, actually saves the human time instead of starting from zero.
AI System Audit & Ongoing Support
AI System Audit & Ongoing Support
Taking over an AI system someone else built — data pipelines, models, and deployment included — starting with an audit of why it's underperforming, expensive to run, or hard to debug, then keeping it retrained, monitored, and improving as data and usage evolve. Most AI systems don't fail loudly: they get less accurate, more expensive, or harder to trust while.
AI Feasibility & Proof of Concept
A focused, time-boxed build to find out whether an AI approach can actually hit the accuracy and cost target needed, before committing to a full build. Most AI budgets get wasted after the fact — months of development before anyone discovers the target was never realistic, or that a simpler, non-AI system would have solved the problem just as well. A clear answer either way in weeks, before serious money is spent.
AI Agents & Workflow Automation
Agents that don't just answer a question but carry out a multi-step task across existing systems: looking something up, updating a record, triggering the next step in a process. A chatbot that only answers still leaves someone to go do the actual work — the difference here is the agent does it, with the tool access and guardrails to be trusted with a real action, not just a conversation.
AI-Powered Search & Retrieval (RAG)
Semantic search across internal documentation, support tickets, or a knowledge base, grounded in the company's own content instead of a generic answer. Most internal search fails not because the answer doesn't exist somewhere, but because keyword search can't match a question phrased differently than the document that already answers it. Built to retrieve based on meaning, and to show where the answer actually came from.
Document Intelligence & Data Extraction
Pulling structured data out of invoices, contracts, scanned forms, and other documents that don't follow a consistent template. Most manual data entry exists because a script built for one document layout breaks the moment it shifts slightly — a new vendor, a slightly different form. Built to handle that variation, feeding directly into the systems that already run the business instead of a spreadsheet someone re-keys by hand. everyone assumes it still works the way it did on day one — and without an evaluation set in place, nobody can tell if a fix actually helped until it's already back in front of users.
Sales & Lead Enrichment Automation
Automatically pulling context on an inbound lead — company, role, intent signals — before a sales rep ever looks at it. Most lead research happens manually, in the five minutes before a call that was booked an hour ago, or doesn't happen at all and the rep walks in cold. Built to have that context ready the moment a lead comes in, not researched on the fly.
Customer Support & Ticket Automation
Resolving common tickets automatically and routing the rest to a human with full context already attached, instead of a customer repeating themselves after a bot gives up. Support automation usually gets judged on deflection rate, which rewards handling easy tickets and frustrating the hard ones — the more useful measure is whether the handoff, when it happens, actually saves the human time instead of starting from zero.
AI System Audit & Ongoing Support
Taking over an AI system someone else built — data pipelines, models, and deployment included — starting with an audit of why it's underperforming, expensive to run, or hard to debug, then keeping it retrained, monitored, and improving as data and usage evolve. Most AI systems don't fail loudly: they get less accurate, more expensive, or harder to trust while.
Approach
How we make it happen
Feasibility, evaluation, and guardrails, sequenced so nothing about accuracy or cost is a surprise after launch.
Use-Case Discovery & Feasibility
Defining the real target — what accuracy or cost bar this needs to hit — and whether a simpler, non-AI system would solve it just as well, based on the data and use case in front of us, not a generic benchmark. Why it matters: this is the step that turns a maybe-good idea into a clear yes or no, before a training run or a sprint gets spent finding out the hard way.
step 01
Data & Access Assessment
Confirming what data, documents, or systems the solution actually needs access to, and whether that access and quality is really there — before assuming it, not after the build starts. Why it matters: an agent or retrieval system is only as good as what it can see — this is usually where a "should be simple" project reveals its real complexity.
step 02
Approach Selection
Choosing between an agent, a retrieval system, a straightforward automation, or a mix — based on the target from step one and what the data actually supports, not whichever looks most impressive in a demo. Why it matters: the flashiest approach and the right one for a given accuracy and cost target are often not the same thing.
step 03
Build & Evaluation
Development against a real evaluation set built from actual questions and edge cases, not curated examples chosen to make a good demo — reviewed and expanded as new failure cases turn up. Why it matters: a system that's never met a hard question will meet its hardest one in front of a real user, not in a review meeting.
step 04
Guardrails & Integration
Wiring in the access, permissions, and safety limits the system needs before it's trusted to touch real data or take real actions — what's automatic, and what still needs a human to confirm. Why it matters: the gap between a demo and a production system is almost entirely in what happens when it's wrong or handling something sensitive.
step 05
Deployment & Monitoring
Shipping with monitoring in place to catch accuracy drift and cost creep from day one — with a clear threshold for when a drop in performance gets flagged, not just felt. Why it matters: AI systems rarely fail loudly — they get quietly worse while everyone assumes it still works the way it did at launch.
step 06
Ongoing Iteration
Improvements prioritized by evaluation results and real usage, not by whichever feature seems most interesting to build next — reviewed on a regular cadence, not case by case. Why it matters: an unmonitored AI feature doesn't stay as good as it was on launch day — it just stays unmeasured.
step 07
use cases
Case study
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