When the off-the-shelf stack stops being enough. We build the bespoke agents, MCP servers, RAG pipelines, and RPA bridges to legacy systems that no SaaS will sell you - with a real eval suite, production guardrails, and a runbook so the thing you ship still works in six months.
Indicative timeline. Final scope and dates agreed after the intro call.
Most pilots that fail do not fail on the model. They fail because the model cannot reach the data, cannot follow the workflow, or cannot be evaluated. The build closes those gaps - your data, your tools, your real cases - so the thing in production matches the thing in the demo.
Most of that knowledge is in your Notion, your Confluence, your Slack, your support tickets - not on the open web. RAG over your real corpus, with citation enforcement and grounded answers, gives your team the answer instead of the search results.
One-shot LLM calls plateau on tasks that need to read state, take an action, observe the result, and continue. The build wires the loop properly: planner, tool-call allowlist, validation, retry, and an eval suite that catches regressions before they ship.
SAP guis, hospital portals, government filings, internal apps with no API - they will not be replaced this quarter. RPA bridges, wrapped with structured retries and a real API in front, let new automation reach the systems your business actually runs on.
The agent that hallucinates once a week erodes trust faster than one that fails loudly. The build ships an eval suite of real cases on day one, runs it on every prompt or model change, and surfaces drift before your users do.
Multi-step agent fitted to your workflow - planner, tool-call allowlist, structured output validation, retries, and dollar caps on action loops. Eval suite of hundreds of real cases, run on every change, with regression alerts before deployment.
MCP servers exposing your CRM, warehouse, repo, internal admin apps, and ticketing to Claude, Cursor, Continue, or any MCP-aware client. Auth, scopes, audit logging, and rate limits baked in. Reusable across every future agent build.
Production-grade RAG: ingest with metadata extraction, chunking tuned to your content, hybrid retrieval with reranking, and citation enforcement so the model cannot answer without showing the source. Hallucination rate measured against an eval set, not assumed.
Bridges to systems with no machine-readable API - SAP guis, hospital portals, government filings, ancient internal apps. Each flow wrapped with screenshot diffing, structured retries, watchdog alerts on UI drift, and where it makes sense, a real API in front so callers do not depend on screen scraping.
Every run logged - prompt, tool calls, outcome - searchable and replayable. Tool-call allowlists, refusal on out-of-policy cases, structured output validation. Cost dashboards by workflow, latency SLOs, and an on-call runbook for the failure modes that matter.
One working session with the operators who run the workflow today. We observe real cases, document the steps, and seed an eval set of fifty real inputs and expected outputs. Model and architecture chosen by end of day three with the trade-offs in writing.
The narrowest useful slice live in your environment - typically one tool, one decision, the eval set running on every commit. Operators see real outputs by end of week one and the eval baseline is on the dashboard.
MCP servers wired for the tools the agent needs to reach, RAG indexed over your real documents with citation enforcement, RPA bridges built where APIs do not exist. Eval set grows to 200+ cases as we discover edge cases on real traffic.
Production guardrails turned on, observability shipped, on-call runbook written, and the agent rolled out behind a feature flag to the broader team. You leave the build with the agent live, the eval suite green, and a baseline you can keep measuring.
Indicative timeline. Highly regulated workflows, novel modalities, or unusually messy legacy data can stretch this; we confirm dates after the kickoff session.
Defined scope, agreed in writing before kickoff. No metered hours, no surprise add-ons, no scope creep mid-build. The first week sets the bar - we ship to it, and the agent runs on real production traffic with eval coverage by week three.
You own the repo, the prompts, the eval suite, the MCP servers, and the model relationship. Production model usage and any RPA worker infrastructure are billed by the provider directly to your account - no markup, no reseller margin, no vendor lock-in to us.
Investment is sized to workflow complexity, integration count, eval surface area, and regulatory scope after the intro call. We come back with one number, in writing.
Start a projectWhen the off-the-shelf vendor cannot reach your data, cannot follow your process, or cannot be evaluated against your real cases. The honest answer most weeks is: stay on the SaaS. We build custom AI when there is a hard reason - your knowledge lives behind a VPN, your workflow is multi-step with branching, your customers expect an answer that no public model has been trained on, or a SaaS would lock you into a per-seat curve that does not scale. We will tell you on the intro call if the build does not pass that bar.
Model Context Protocol is the open standard Anthropic shipped for letting AI models talk to your tools - your CRM, your warehouse, your repo, your internal admin app - in a typed, auditable way. An MCP server is the integration that exposes a specific tool to any MCP-aware model (Claude, Cursor, Continue, etc.) without having to glue a model to it bespoke every time. The build delivers the MCP servers your team will lean on for the next two years, with auth, scopes, and audit logging built in.
Most RAG demos work on a clean PDF. Production RAG fails on the messy reality - PDFs that are scans, Notion pages with stale frontmatter, Confluence sprawled across three spaces, support tickets with screenshots. The build invests in the boring layer: ingest with metadata extraction, chunking tuned to your content, evals on real questions your team actually asks, and reranker plus citation enforcement so the model cannot answer without showing the source. Hallucination rate is measured, not assumed.
It can be. The build minimises that surface: we use APIs everywhere they exist; RPA is the last-resort bridge to systems with no machine-readable interface (SAP guis, hospital portals, government filings, ancient internal apps). Every RPA flow is wrapped with screenshot diffing, structured retries, and a watchdog that pages an owner if upstream UI changes. Where possible, we add a real API in front of the RPA flow so callers do not depend on screen scraping directly.
Three layers. (1) Eval suite: hundreds of real cases run on every prompt or model change, with regression alerts. (2) Production guardrails: tool-call allowlists, structured output validation, refusal on out-of-policy cases, dollar caps on action loops. (3) Observability: every run logged with prompt, tool calls, and outcome - searchable and replayable. The agent does not get to do things you have not authorised, and you can see exactly what it did when something looks off.
In infrastructure you control. Agents, MCP servers, vector stores, and RPA workers deploy in your cloud account (AWS, GCP, Azure). Models can be Claude, OpenAI, or self-hosted (Llama, Mistral) depending on data sensitivity and cost profile - chosen per workload, not pre-baked. Production model usage is billed by the provider directly to your account. Your data does not train external models.
Three options: (1) take the repo, evals, and runbooks and run it internally, (2) keep us on retainer for prompt tuning, eval expansion, model upgrades, and outage response, (3) scope a follow-on build (a second agent, a wider RAG, a customer-facing version). No pressure to continue, no vendor lock-in.
Tell us what the off-the-shelf stack cannot do today - which workflow, which data, which legacy system. We will come back within one business day with the next step.
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