Trained in the particulars.
Abraxas trains AI agents on your actual workflows — the systems, the rules, the unwritten ways — and deploys them as employees.
Good morning. Overnight:
The escalated item: a vendor mismatch over your threshold. The full file is attached — history, variance, and a recommendation.
A senior hire, minus the ramp-up.
An agent initiated in the particular ways of one business — a colleague, not a vendor.
Hand off a task in a sentence.
From Slack, email, or WhatsApp. The agent breaks it down, does the work inside your systems, and reports back with the file attached.
Ask what a colleague would know.
The agent answers from your data and cites the source.
Add the agent to a project as you would any teammate.
It takes its share of the recurring work and keeps you informed.
Give them charge of the recurring.
Quotes and proposals
drafted from the request, the client's history, and your pricing rules; the seller reviews and sends.
Month-end close and reporting
accruals, adjustments, and the close package, each number with its source.
Escalations and exceptions
the full file assembled, the house rules applied, the options drafted for your decision.
Recurring reports
pipeline, inventory, and ops summaries assembled from your databases and files, delivered to an inbox or a channel.
Document processing
invoices, purchase orders, statements, and contracts extracted into structured data in your systems.
Order entry
email and PDF purchase orders keyed into the ERP, with an exceptions queue for humans.
Reconciliation
invoices against the bank feed, vendor statements against the ledger; mismatches escalated with context.
Inbox and ticket triage
routine requests sorted and answered; the rest escalated with a summary.
Orchestrated in the cloud. Executed on your premises.
Live in production in two weeks.
Most AI pilots stall in planning. We begin with a minimum viable workflow drawing on our production-tested libraries and move straight into producing results.
Feasibility assessment.
Do your processes have the foundations for automation — standardization, high-frequency repetition, redundant manual labor? You receive the risks, a prioritized roadmap, and schedule estimates.
Scope freeze.
We lock the scenarios, functional modules, input/output rules, delivery boundaries, milestones, acceptance criteria, integration scope, and a change-approval process. No scope creep or runaway costs.
Deployment.
Model integration, retrieval systems, and custom Skills built for your working context. Deployment planning, user-experience design, and engineering.
Testing and delivery.
A phased, tiered rollout in a small set of real scenarios first, so bottlenecks, data anomalies, and compatibility issues surface early. Hands-on training for your staff, functional handover, roles confirmed.
Iteration and support.
Going live is not the finish line. Under our Result-as-a-Service model a dedicated remote deployment engineer follows your system as usage scales, new scenarios appear, costs shift, and models change.
Every workflow ships with its ledger.
We benchmark each automation against the process it replaces and hand you the before-and-after.
40-person company. Sixty quote requests a month, all written by the owner and two senior sellers. One agent now drafts each quote from the brief, the client's history, and the pricing rules, and posts it for review with sources listed. Out-of-rule pricing escalates to the owner. The seller edits and sends.
12-person business. No finance hire — the owner closed the books on evenings from the bank feed, card statements, and invoices sitting in email. One agent now matches transactions, chases missing invoices, categorizes to the ledger, and posts a Monday cash summary with each number's source cited. Answers ledger questions in Slack. Anything unmatched escalates with the file attached.
65-person company. Ninety escalations a month — complaints, refunds, contract exceptions — each landing on the owner's desk after an hour of digging. One agent now assembles the full file per case (order and ticket history, contract terms, correspondence, money at stake), applies the house rules, and posts the options to Slack. In-rule cases it settles and logs; the rest wait for the owner's judgment.
Which workflows are worth automating?
The strong candidates are highly repetitive, rule-clear, and time-consuming every week. Document processing, recurring reports, and content publishing usually meet all three. The assessment maps your processes and returns a prioritized list with an hours-saved estimate for each.
Will this work with our existing systems?
Yes. We build around your current stack rather than against it. Custom ERP, SaaS finance tool, in-house CMS — the integration layer connects to what you already have. No migration, no rebuild.
How do the agents actually work?
Multi-entry-point orchestration plus local secure execution. You start a task from Slack, email, or WhatsApp. A cloud orchestrator understands the request, breaks down the process, schedules the work, and reports results. A secure execution node inside your environment does the work — Excel, Outlook, allowlisted folders, databases, internal systems, within the scope you authorize — and the output lands where you need it: local machines, shared drives, your servers, email, or your collaboration platform.
What does "live in two weeks" really mean?
Two weeks of actual development and build time to a minimum viable workflow running in production — not a proof of concept. Client-side waiting, ie data delivery, API permissions, environment preparation, model access, scope changes, internal approvals — is not counted. We agree on the short list of client-side dependencies together in the working session.
How do you keep quality high while moving fast?
Scope discipline and clear acceptance criteria first; then strict linting, type safety, automated checks, and testing in a simulated production environment. Preview environments let your staff review quickly; we dogfood core flows internally and run error monitoring to catch regressions early.
Which AI models do you use?
Claude, OpenAI, GLM, Kimi, DeepSeek, and whatever your enterprise platform requires. We don't lock you into one model or vendor; we recommend by use case, budget, and architecture.
How do you handle data privacy and security?
Every pipeline is scoped to its data requirements. We deploy on-premises or in a cloud you own, mask data at the extraction layer, and where needed ensure outputs never leave your infrastructure. Regional privacy and compliance requirements are part of scoping.
Do you help with deployment, monitoring, and analytics?
Yes. We set up the cloud environment, CI/CD, and observability — logging, distributed tracing, error monitoring — and configure meaningful analytics events and funnels so iteration is data-driven from your first users.
What happens after launch?
Going live is not the finish line. Under our Result-as-a-Service model a dedicated remote deployment engineer follows your system as usage scales, new scenarios emerge, costs shift, and models change.
We already have an automation or MVP that's shaky. Can you fix it?
Yes. We run a rapid review across code, user experience, performance, and security, draw up a remediation plan, and deliver improvements in short, results-oriented sprints — flow redesign, speed and accessibility, refactoring fragile code, hardening for scale — with a migration plan to minimize downtime.
Anyone can rent intelligence. Knowing your business is another matter.
Bring one recurring process.
Leave with a plan.
In a working session we look at your highest-friction recurring processes, identify the top candidate, and outline the first two weeks.