AI Assistants · Workflow · Predictive
Cut hours of repetitive work from every department — and give your team AI tools that deliver real, measurable results in the real world.
Core tech we use
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HF
Live signal
We engineer production AI systems scoped against measurable business outcomes — hours saved, errors reduced, decisions accelerated. No demos, no buzzwords.
4 signals
6 measurable outcomes
We sit with your team, look at the repetitive work, and rank candidates by ROI, technical feasibility, and data readiness. Out: 2–4 prioritized use cases.
Sample data, ground-truth labels, and a proof-of-concept on a small slice — to validate accuracy before committing to a full build.
4–8 week pilot delivering one production-ready use case end-to-end, with a measurable target (hours saved, accuracy %, response time).
Move the pilot into your stack — auth, monitoring, fallback logic, human-in-the-loop review for edge cases, and audit logging for compliance.
Hands-on training for end users, prompt engineering playbooks, and an internal ops manual — so the system isn't a black box.
Accuracy dashboards, drift detection, periodic retraining, and a feedback loop from users to the model — so it gets better over time, not worse.
Six scenarios drawn from our pipeline — each with the problem, our approach, the stack, and the outcome we'd drive.
10 staff manually read bank statements + pay slips; 6% error rate, 48-hr cycle.
Tools used
OpenAITXAWS Textract
PythonFastAPIEngineers interrupted daily for routine SOP questions — and the SOP library nobody can search.
Tools used
LangChainPineconeCClaude
SlackFast SKUs run out; slow ones overstock; churn-risk customers leave silently.
Tools used
TensorFlowPython
SnowflakeLLooker10K tickets/month, 80% are status/return/exchange questions — humans handling everything.
Tools used
OpenAI
Shopifyn8nZdZendeskJunior associates spend hours flagging clauses in NDAs and MSAs — slow, inconsistent.
Tools used
PythonWMS WordVoice + photo claims rekeyed into the claims system by humans — bottleneck on day-zero.
Tools used
AWSWe build systems that survive contact with real users, not Jupyter notebooks. Auth, latency, fallbacks, cost controls and observability are part of every build.
OpenAI, Anthropic, Llama, Mistral, fine-tuned in-house models — we pick what fits the task, the budget, and your data residency requirements.
On-prem, VPC-isolated, EU-resident, or fully air-gapped deployments are all on the table. Your data never trains a public model.
We're upfront about where AI is reliable and where it isn't. For high-stakes calls — credit, medical, legal — we design the review workflow alongside the automation.
Every use case ships with a target metric: hours saved, accuracy %, response time. If the number doesn't move, we don't bill the next phase.
You get applied ML scientists and production engineers on the same pod — not a research team that hands a model over the wall to someone else to ship.
Our AI-assisted delivery model means these timelines are realistic — not padded.
4–8 week fixed-scope pilot on one prioritized use case, with a measurable target.
Best for: validating value before committing to a long-term program.
ML scientist + ML engineer + integration engineer, embedded with your team monthly.
Best for: rolling out multiple use cases over 6–12 months.
We build the AI capability in-house and hand it over to a team we help you hire and train.
Best for: organizations who eventually want AI to be a core internal competency.
Yes — and that's the norm, not the exception. A meaningful share of every AI engagement is data cleanup, labelling, and pipeline work. We scope it up front and don't pretend it's optional.
No. We use enterprise API tiers (OpenAI, Anthropic, AWS Bedrock) with zero-retention contracts, or fully self-hosted open-source models. Your data stays your data — full stop.
Depends on the task. For most knowledge-work tasks, frontier APIs are cheaper and better. For domain-specific accuracy, regulatory constraints, or massive volumes, fine-tuned open-source wins. We model both before recommending.
We design around the failure mode: retrieval-augmented generation grounded in your sources, confidence thresholds, human-in-the-loop review for low-confidence outputs, and full audit logs. For high-stakes use cases we never deploy without a review layer.
Pilot: fixed price, 4–8 weeks. Production rollouts: fixed price per use case or monthly pod. We also include cost modelling for API spend (tokens, embeddings, inference) so you can forecast unit economics before you commit.
Yes. Our build approach treats integration as a first-class layer — Salesforce, HubSpot, Zoho, SAP, Oracle, Microsoft Dynamics, Snowflake, BigQuery, and bespoke in-house systems are all in scope, scoped during discovery rather than bolted on afterward.
Tell us where you are. We'll scope it, estimate it, and tell you exactly how we'd approach it.
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