We integrate AI agents and retrieval systems into your ERP, CRM, procurement and finance stack — so the work gets done in the systems your business already depends on, not in a separate tool nobody opens.
Most enterprise AI projects do not fail because the model was wrong. They fail because the data sat in five systems that did not speak to each other, permissions were never modelled, and nobody agreed what number the project was supposed to move. AI is only as good as its access to your ERP, procurement, contract and finance data — and that access is an engineering problem, not a prompting one.
That is the work we do. We build AI agents and retrieval systems for operations-heavy businesses — proven in background screening, B2B trade and workflow automation — and we connect them to the systems of record you already run rather than proposing to replace them.
Workflow automation, not a standalone chatbot. Each of these replaces work a person is doing manually today.
RAG pipelines over contracts, supplier documentation and policies — obligation tracking, renewal alerts and cited answers pulled from the document itself rather than generated from memory.
Automated supplier, vendor and candidate onboarding: document collection, validation against source systems, exception routing to a human when something does not match.
Matching invoices to purchase orders and receipts, flagging the exceptions that need judgement, and passing the rest straight through — the work that quietly consumes a finance team.
Assistants that answer questions across your own records — “which contracts expire next quarter?”, “which suppliers meet our requirements?” — grounded in your data with role-based access enforced.
Models that score vendors, applicants or transactions using internal records plus external signals, with the reasoning exposed so a human can audit and override it.
Agents that act across ERP, CRM, finance and internal tools through secure tool-calling — completing the process, not just describing it.
The gold entries are integrations we have delivered and run in production. The rest are platforms we build against through their standard APIs. We keep the two lists separate on purpose — if we have not shipped against your platform before, you will hear that on the first call rather than after signing.
A background-screening platform used by Manpower, CRST and Employbridge, covering 85% of the US population. Built to sit inside the HR and staffing systems its clients already ran — the integration was the product, not an add-on.
100+ AI agents deployed in production on a single client platform, delivering a measured 103% efficiency gain. Agents doing operational work, not a chatbot answering questions about it.
A B2B trade platform connecting buyers and suppliers: onboarding, catalogue, pricing and order workflow. The procurement-side problem, solved end to end for a Gulf client.
Answered here so your security and compliance review does not stall the project three months in.
An assistant must never surface a document the person asking is not cleared to read. We apply your permission model at the retrieval layer, not as a filter on the output — so the model never sees what the user cannot.
Every answer links to the record it came from. If the system cannot find a grounded answer, it says so rather than inventing one. This is the difference between a tool people trust and one they quietly stop using.
Payments, contract commitments and status changes route to a named approver. Agents draft and prepare; people decide what actually happens.
Every retrieval, decision and action is logged with its inputs. When someone asks six months later why the system did what it did, the answer exists.
Where data must stay in a region — UAE, UK, EU — we deploy there or on your own infrastructure. We will tell you plainly which model options remain available under a strict residency requirement, and what each costs you in capability.
Your documents and records are not used to train third-party models. We configure enterprise endpoints with that guarantee and put it in the contract.
A full replacement programme is the most common way this work fails. We ship one capability, prove it against a number, then extend.
We start with how the work actually happens — including the exceptions people handle manually and never document. That is where automation succeeds or fails.
We audit what data exists, where it is fragmented, and what integration and permission constraints are real. Most AI projects fail here, before any model is chosen.
One capability into production — contract search, onboarding, invoice matching — measured against a number agreed up front. Not a platform rollout.
Once the first capability is demonstrably paying for itself, we extend to adjacent workflows. Scope grows from results, not from the original proposal.
“The developer demonstrated a strong understanding of both Python and PHP. API endpoints were integrated efficiently, ensuring seamless backend functionality. A reliable full-stack resource with solid communication.”
“Handled OAuth2 authentication and Zoho integration smoothly. Understood the use case clearly and implemented the solution flawlessly. Will definitely hire again for future automation tasks.”
“Awesome to work with. All requests were handled quickly and efficiently. Delivered exactly what was needed in record time.”
These are the questions an AI development partner should be able to answer without hedging. Ours are below.
Integrate. Replacing a working system of record is expensive, slow and rarely necessary — the value is almost always in the layer above it. We connect through APIs and secure tool-calling so your ERP, CRM and finance systems stay the source of truth, and the AI operates on top of them. We have shipped production integrations with Oracle, Workday, Bullhorn and UKG.
Our closest work is Hyves, a B2B trade platform built for a Dubai client that connects buyers and suppliers through onboarding, catalogue, pricing and order workflow — the procurement side of the same problem, and it increased dealflow tenfold. Asurint is our strongest evidence for verification and compliance workflow at enterprise scale. We have not delivered a Coupa or Ariba implementation, and we will say so rather than stretch a case study to fit.
Claude, GPT, Gemini and open-weight models, deployed directly or through Azure OpenAI and AWS Bedrock. Model choice is an engineering decision driven by your data residency requirements, latency, cost and the specific task — not by which vendor we have a relationship with. We build so the model can be swapped without rewriting the system.
Your permission model is applied at the retrieval layer, so the model never receives documents the person asking is not cleared to see — filtering the output afterwards is not sufficient and we do not rely on it. Beyond that: no training on your data, enterprise endpoints with that guarantee contractually, full audit logging of every retrieval, and human approval on any consequential action.
We agree one number before development starts — hours removed, onboarding time, invoice exception rate, cost per transaction — and instrument the system to report against it from day one. On our AI co-worker platform that number was roughly $80,000 per month in savings and a 103% efficiency gain. A project that ships on time and moves no number has still failed.
Often you should extend what you have. If 80–90% of your requirements can be met by configuring Dynamics, NetSuite, Odoo or your existing ERP, that is faster, cheaper and easier to maintain than a bespoke build — and we will tell you so even though it is the smaller engagement for us. Custom is the right answer when your process is genuinely a differentiator, or when the gap between platform and reality is where all your manual work lives.
Our own engineers write it — we do not subcontract delivery. All source code, infrastructure and IP transfer to you, written into the contract before work starts. We hand over documentation and access, not a dependency on us.
A paid discovery phase of one to two weeks produces process maps, functional and integration requirements, data migration plan, technical architecture, delivery roadmap and a fixed scope. That document is yours — you can take it to other vendors for comparable proposals. We then quote a fixed price and a fixed date against it.
Book a call and we will map where AI would actually pay for itself in your stack — and tell you honestly if configuring what you already own would do the job instead.