Six capability lines, all deployed inside your own environment, all measured against a business outcome. No pilots that quietly die in a sandbox.
It's almost never about the model. It's about the data pipeline, the retrieval quality, the guardrails and the operational plumbing around it. That's the part we're good at — and the part most AI vendors skip.
Support, sales and internal agents grounded in your own knowledge base, with clean handoff to a human when confidence drops.
Your process, rebuilt as orchestrated automation with a model in the loop where judgement is genuinely required.
Domain-tuned models running in your VPC. Your data never leaves the perimeter, and your jargon is understood natively.
Agents that read, decide and act across your systems — with approval gates wherever the consequence is irreversible.
Forecasting, churn and anomaly models wired directly into the dashboards your leadership already opens every Monday.
Invoices, contracts, claims and forms extracted into structured data — with confidence scores and a review queue for the edge cases.
An agent that actually knows your business. We ground it in your documents, tickets, product data and internal wikis — then measure it against real historical conversations before it ever faces a customer.
This is the service most clients end up buying. We sit with your team, map the process end to end, and find where information is being copied between systems by a human who has better things to do.
# Runs on every purchase order received on: po.received do: - extract: line_items, terms, currency ← pdf / email / edi - match: catalog.sku + contract.price - flag: variance > 2% ? needs_approval - post: erp.sales_order.create() - notify: teams(#ops) + email(buyer) audit: every step logged, replayable
When a general model isn't precise enough for your domain, we fine-tune. Legal clause analysis, medical coding, industrial fault classification, financial reconciliation — the vocabulary matters and generic models get it wrong.
Your cloud tenancy, your on-prem GPU, or a private dedicated deployment. No data used for third-party training.
A held-out test set with task-specific scoring, so you can see whether v2 actually beat v1.
Cheap models handle the easy 80%, the tuned model takes the hard cases. Inference bills drop, quality holds.
Output schemas, PII redaction and refusal policies enforced at the service boundary.
The three capability lines that tend to follow once the first two prove themselves. Same architecture, same governance, more of your process automated.
Not a chatbot that suggests things — an agent that reads a queue, makes a decision, writes to the system of record and moves on. Every irreversible action sits behind an approval gate until you're confident enough to remove it.
Forecasting, churn risk, demand planning and anomaly detection — wired into the dashboards your leadership already opens. We care about whether a prediction changes a decision, not about model leaderboards.
Invoices, contracts, claims, purchase orders and handwritten forms extracted into clean structured data. Confidence scoring means the edge cases route to a human queue instead of silently corrupting your records.
An AI feature is a software system like any other — it needs pipelines, tests, monitoring and a rollback plan. We build it the unglamorous way, which is why it survives contact with production.
Held-out test sets, task-specific scoring and versioned releases — so you can prove v2 beat v1 instead of assuming it did.
When data can't leave the building, we deploy models on-premise or at the edge — same governance, no egress.
Five layers, every one of them monitored. The model is one box in the middle — and by far the least interesting one.
Ingestion from ERP, CRM, helpdesk, file stores and databases. Cleaning, chunking and embedding, with lineage tracked per record.
Hybrid vector and keyword search with permission-aware filtering, reranking and citation metadata on every retrieved passage.
Model routing between hosted and fine-tuned endpoints, tool calling, structured output schemas and deterministic fallbacks.
PII redaction, prompt-injection defence, refusal policy, output validation and an immutable audit trail of every decision.
Evaluation harness, drift alerts, cost dashboards, rollback to the previous version in one command. This is what keeps an AI feature alive after month three.
Decision logs, model cards, data-flow diagrams and a written policy for human review. When compliance asks how the system decided something, you'll have the answer.
Fair questions, all of them. Here are the honest answers.
Book an AI scoping callInto your own cloud tenancy or your on-premise infrastructure. Retrieval indexes, vector stores, logs and model weights all stay inside that boundary. We do not train on your data, and no third-party provider sees it.
You can't stop it entirely — but you can contain it. We force citations, validate output against schemas, score confidence per response, and route anything below threshold to a human. For workflows, the agent acts only on structured validated fields, never free text.
A two-week discovery to pick one workflow with a clear baseline, then a four-to-six week build to production. You'll have a working system and a measured before-and-after before committing to anything larger.
No. Most of our deployments are operated by the same IT team that runs your infrastructure. We hand over runbooks, dashboards and training — and stay on retainer for changes.
It will, eventually. That's exactly why we build an abstraction layer over model endpoints with an evaluation harness attached. Swapping the underlying model becomes a config change you can validate in a day — not a rewrite.
Thirty minutes, one workflow, and an honest answer about whether AI is the right tool — sometimes it's just a better script, and we'll tell you that.