AI & intelligent automation
AI that's wired into your systems, not bolted onto a landing page.
- Engagement
- Assessment, then fixed-scope pilot, then retainer
- Typical timeline
- 2-week assessment, 6–12 weeks to production pilot
- Starts at
- $9,500
The assessment stands on its own. Several clients have taken it and correctly decided not to build anything yet.
Book a callYou're probably reading this because
If two or more of these describe your situation, a 30-minute call will be worth your time.
- Leadership wants an AI strategy and nobody can say which process it should touch first.
- You ran a promising pilot in ChatGPT and have no path from there to production.
- Staff spend hours reading PDFs, invoices, or contracts and retyping the contents.
- You bought an AI product that doesn't know anything about your business's own data.
- You need to be certain the model can't leak customer data or invent an answer that costs you.
Concretely, what we deliver
Written as deliverables rather than activities, so you can tell whether you received them.
- 01
An honest use-case assessment
We rank candidate processes by hours saved against implementation risk, and we say plainly which ones AI should not touch. Deterministic code beats a language model for most tasks, and we'll tell you when that's the case.
- 02
Retrieval over your own knowledge
Your documents, tickets, policies and records made queryable, with answers that cite their source so a human can verify them. Permissions respected at the retrieval layer, not bolted on after.
- 03
Document and data extraction pipelines
Invoices, contracts, statements, and forms turned into structured records with confidence scores, plus a review queue for anything the model isn't sure about.
- 04
Evaluation harnesses
A test set drawn from your real cases, scored automatically on every change. This is the difference between a demo and a system you can trust. Without it you cannot tell whether a prompt change made things worse.
- 05
Cost, latency and failure controls
Token budgets, caching, timeouts, retries, and a defined fallback for when a provider has an outage. AI features fail differently from ordinary code and need to be engineered for it.
- 06
Human-in-the-loop by default
For anything consequential, a person approves before it takes effect. We design the review interface as carefully as the model integration.
Our approach, and why
The reasoning matters more than the method. If you disagree with the reasoning, we should talk before you hire anyone.
The model is the easy part
Calling an LLM is a few lines of code. The engineering is everything around it: getting clean data in, validating what comes out, handling the 5% of cases that go sideways, and keeping cost predictable. That's where an AI project succeeds or quietly fails.
Start where a wrong answer is cheap
First deployments go somewhere a mistake is recoverable: drafting rather than sending, suggesting rather than deciding. You build organizational trust and a real evaluation set before AI touches anything irreversible.
Measured against a baseline
Before launch we record how long the process takes and how often it's wrong today. Without that number, nobody can tell whether the AI helped, and the project becomes a matter of opinion.
Provider-portable
We keep model calls behind an internal interface so switching providers is a configuration change. This field moves too quickly to hard-wire your business to one vendor's API.
Technologies we use for this
- Claude API
- OpenAI API
- Python
- TypeScript
- LangChain
- pgvector
- PostgreSQL
- Redis
- AWS Bedrock
- Azure OpenAI
- Celery
- Next.js
Selected per project against your team's existing skills and hiring market, not against what's fashionable. If your system runs on something not listed, ask.
What changes for you
- Hours of reading and retyping per week replaced by a review-and-approve step.
- Institutional knowledge becomes searchable instead of living in six people's heads.
- A defensible answer to your board on what AI is doing for the business.
- Cost per transaction that you can actually forecast.
What people ask about ai & intelligent automation
No. We use enterprise API tiers where the provider contractually excludes your data from training, and we document exactly which data reaches which provider so your compliance team can review it. Where that isn't acceptable, we can run open-weight models in your own cloud.
Several layers: retrieval so answers are grounded in your documents, citations so a human can check, schema validation on structured output, confidence thresholds that route uncertain cases to a person, and an evaluation set that catches regressions. We also scope AI away from tasks where a confident wrong answer is unacceptable.
We model this during the assessment and give you a cost per transaction before you commit to building. Most business workflows land in fractions of a cent to a few cents per operation; caching and routing cheap tasks to smaller models usually cuts the naive estimate substantially.
Sometimes not, and we'd rather say so in week one than bill you for six months. Well-defined rules, exact calculations, and anything requiring auditable determinism should be ordinary code. AI earns its place on unstructured language, messy documents, and judgement-heavy triage.
Often combined with
Custom Software Development
Applications built for how your business actually works: the workflows no off-the-shelf product will ever match.
Systems Integration
Connect your CRM, ERP, PMS, billing and internal tools so data moves once, automatically, and stops being retyped.
Dedicated Teams
Vetted nearshore engineers who join your standups, your repo, and your roadmap, and who stay long enough to build real context.
Let's talk about your ai & intelligent automation project
Thirty minutes with an engineer who can scope it. You'll leave with a view on approach, a budget range, and an honest answer about whether we're the right firm, including when we aren't.