RAG, agents and workflow automation built into the software your business already depends on — with the retrieval, evaluation, access control and cost limits that decide whether an AI feature survives past the demo.
Answers that come from your documents, your database and your rules, with a citation back to the source — not from whatever the model happens to remember. We do the unglamorous half: extraction from the formats your data is actually in, including PDF tables and scans; chunking and metadata that match how your people ask questions; a vector store that sits next to the system of record instead of becoming a second copy that drifts; and access control applied at retrieval time, so a query can't return a record the person asking isn't allowed to see.
Software that takes an action, not just software that answers. Back-office paperwork, CRM and ERP synchronization, document intake and classification, inbound triage and routing, scheduled reporting, outreach and follow-up sequences, and chat or voice front ends where the conversation is the interface. Every agent ships with a bounded tool set, a defined escalation path to a human, and a log you can audit afterward.
The hard part here isn't the model — it's shipping into a system with real users and real uptime commitments. Architecture review before any code. Feature flags and staged rollout. A fallback path for when the model or the provider is down, because both will be. A human in the loop wherever an AI output has consequences. And an evaluation set built from your own examples, so the next change can be measured instead of argued about.
Provider routing, so one vendor's outage or price change isn't automatically yours. Open-weight or on-device models where data residency, latency or unit economics require them. Caching and model tiering to bring the per-request cost down. MCP servers to expose your internal tools and data to agents through one controlled interface instead of six ad-hoc integrations. Monitoring on latency, spend and failure rate, per feature.
If the system underneath isn't in a state you can integrate anything into, start with Legacy Modernization →. If the data is PHI and a clinician acts on the output, the constraints are different — that's Healthcare Software →
By the time a company talks to us, the model usually works. What's missing is one of four things, and every one of them is ordinary engineering rather than machine learning.
There's a difference between having data and having data something can retrieve from. Values locked in merged PDF table cells. Three spellings of the same product name. A wiki where the current answer and the 2019 answer sit side by side with no way to tell which is which. Retrieval quality is a data-engineering result, not a prompt result, and it is where most of the real work is.
Without a fixed set of your own examples and expected outputs, "is it better now?" is a matter of opinion, and every prompt change is a gamble. We build the evaluation set from your actual cases first and measure every change against it — the same discipline we use on our own products before a model change ships, not a one-time report.
A retrieval layer that can read everything will eventually answer a question it shouldn't. Permissions have to be enforced at retrieval time, per user, against the same rules your application already uses — not filtered afterward, and not delegated to an instruction in a prompt telling the model to be careful.
An AI feature has a per-request cost and a per-request latency, and both scale with usage in ways a demo never shows. Deciding the budget before the architecture is what makes the difference between a feature you can price and a bill you discover.
The AI feasibility review is the answer to all four at once: we look at your system, your data and the workflow you want to automate, and come back with what's actually retrievable, what the evaluation set should measure, where the access-control boundary has to sit, and what the thing will cost per request once it's live. Fixed scope, fixed fee, fixed dates.
Your product or internal platform is in production, people depend on it, and now it needs AI in it. Three ways this usually starts:
Support, sales and operations spend their day looking things up in systems that don't talk to each other. We build retrieval over the real sources — the database, the document store, the ticket history — with citations, so the answer can be checked instead of trusted.
Your customers are asking for it, or your competitors shipped it. We do the architecture review first, build behind a feature flag, ship to a slice of traffic, and put the evaluation set and the cost monitoring in place before the feature is on by default.
It works, mostly, and nobody can prove it. We add the evaluation set, the observability, the access-control boundary and the provider fallback that weren't there the first time — inside your codebase, without a rewrite.
Old systems are not a blocker here. Most of what we do sits behind an integration layer around what already works, and taking over software other people wrote is the business we've been in since 2007. If the system itself is the problem, we'll say so and start there.
There's nothing to integrate with yet — there's a workflow that runs on people, a shared inbox and a spreadsheet. That's a different starting point and a shorter one.
One workflow, measured. How many times a day it runs, how long each run takes, where it goes wrong, and what the wrong version costs. Automation without that number is a guess about ROI.
The agent and the integrations it needs — the CRM or ERP it reads and writes, the documents it has to parse, the channel it talks on, the point where it hands off to a person. One workflow in production before the second one is scoped.
Ship an agent with unbounded access to your systems and hope the prompt holds. Bounded tools, explicit escalation, full logging, and a person who owns the exceptions.
If what you want is specifically an agent build — back-office, support, voice, an enterprise copilot — the detail on types, process and stack is on AI Agent Development →
In August 2025 we collected and coded 542 AI-agent job postings on Upwork. This is demand-side data: what companies were paying to have built, not what got delivered and not what worked. We publish it because it shapes what we offer, and because most vendor pages describe a market nobody measured.
| Most-requested use case | Back-office automation — 15.2% (invoicing, purchase orders, CRM-to-ERP sync) |
| Second | Customer support and live chat — 14.8% |
| Then | Voice and call automation 7.6% · lead and outreach 7.4% · content generation 7.2% |
| Leading industries | Marketing and sales 17.6% · enterprise software 13.2% · healthcare 8.1% |
| Languages | Python 52% · Node.js 17% · Go 12% |
| Orchestration | LangChain 55.6% of framework mentions · CrewAI 9.5% · AutoGen 5.6% |
| Data layer | Pinecone 22.6% of vector-store mentions · PostgreSQL 18.8% |
| Model providers | OpenAI in over 70% of projects · Claude 16.6% |
| Rates | Clustered at $20–60/hour; weighted average $35.08/hour; outliers from $5 to $600 |
| Timelines | 36% expected 1–3 months at under 30 hours a week |
Two things we take from it. The money is in unglamorous internal work, not in consumer-facing novelty — the top two categories together are almost a third of demand. And the weighted average rate says most of this market is buying hours, not outcomes, which is exactly why so many of these projects never reach production. We'd rather sell you the review and the first workflow.
Full write-up: AI agent development trends: original research of 542 projects →
One system, one workflow. What's retrievable, what the evaluation set measures, where the access-control boundary sits, what it costs per request in production.
Fixed fee from $4,500 · 2–3 weeks
One integrated feature, behind a flag, with the evaluation set and the monitoring.
For an AI feature that's already live and unmeasured.
Routing, caching, model tiering, open-weight or on-device where it pays for itself.
One workflow, measured: volume, handling time, error rate, what the errors cost.
Fixed fee from $4,500 · 2–3 weeks
The agent, its integrations, its escalation path, its logs.
Scoped after the first one is running, not before.
Retraining, prompt and model updates, new workflows as they earn their place.
The review and the audit are fixed-fee at the price above. Everything after them is quoted once we know what's actually there — because the honest number depends on your data, not on our rate card.
Company-wide totals across all industries since 2007, not AI-specific.
The contracting entity is an Estonian company; the engineering team is in Poland. We'll answer the data-residency, subprocessor and DPA questions in writing before you have to ask twice. Where the cleanest answer is that your data never reaches us at all, we work inside your infrastructure and against your provider accounts.
We pick per project and we'll tell you why. Vendor independence is a design goal, not a slogan — see the cost and provider-independence package above.
Start with the review
One system, one workflow, fixed scope and fixed dates. You get what's retrievable, what to measure, where the boundary sits and what it costs per request — and it's yours whether or not you build with us.
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