Primary research for the answer-engine era, our most-cited piece.
Five constraint numbers locked before build. Six stages from discovery to hand-off.
Enterprise AI solutions are governed, integrated AI features that run inside your security boundary at scale, and Resourcifi is the partner that builds and ships them. We build copilots, service-desk agents, knowledge search, and finance-ops automation into SAP, Salesforce, Workday, and ServiceNow, behind evaluations, guardrails, audit logs, and cost controls, on private cloud, on-prem, or hybrid. We treat the gap between a working demo and a production feature as the real engineering problem. About a third of our work is Production Recovery, fixing AI features other vendors abandoned in proof of concept. As the AI development company behind these builds, we ship enterprise generative AI that holds under real load.
An enterprise AI feature is only as trustworthy as what it retrieves, so we build the data layer first.
The best enterprise AI feels native, streams in real time, and respects role-based access.
Real enterprise automation chains tools and decisions, so we build approvals and limits in from the start.
Production-First AI means an eval gate, full tracing, and a deploy that blocks on a regression.
Shipping AI in the enterprise means RBAC, DLP, prompt-injection defense, and an answer to who saw what.
Most enterprise AI features do not fail on the demo, they fail on the way to production, and inside a Fortune 1000 the model is rarely the problem. A serious partner closes that gap on purpose. First, retrieval is engineered before the prompt: clean ingestion from SharePoint, Confluence, and the systems of record, embeddings, hybrid search, and rerankers, with permission-aware retrieval so an employee only ever sees what their role grants. Second, quality is measured, not asserted. We build a golden-set and LLM-judge evaluation harness and wire it into CI/CD, with three rollback paths wired before any traffic flips, so a change that regresses accuracy or safety is blocked and reversible. Third, the feature is defended and accounted for: RBAC and DLP guardrails, full tracing of every prompt and tool call, model-version pins and a prompt registry, and hard cost and latency budgets, so internal audit and the CISO get the same answer about where an output came from and who saw it. That is what Production-First AI means, and it is decided in the architecture long before launch.
Permission-aware retrieval over SharePoint, Confluence, Google Drive, Salesforce, and ticket systems, scoped at query time.
Metric: answer quality →Inline assistants in Microsoft 365, Google Workspace, Slack, or internal portals, grounded by enterprise data and governed by SSO and DLP.
Metric: adoption →Tier-1 deflection on password resets, access requests, provisioning, and runbook lookups in ServiceNow and Jira Service Management.
Metric: deflection rate →Clause extraction, redlining, supplier-risk summarization, and renewal triage, integrated with Coupa, Ariba, and SAP.
Human-in-the-loop →Invoice triage, journal-entry classification, and three-way match exceptions across SAP, Oracle, NetSuite, and Workday Financials.
Metric: exceptions cleared →Account summaries, next-best-action, and meeting prep grounded in Salesforce, with structured output your CRM can render.
Metric: pipeline hygiene →Policy and benefits Q&A grounded in your handbook and HRIS, with answers cited to the controlling document.
Metric: ticket deflection →Ask-your-data interfaces that turn questions into queries against the warehouse, with row-level security preserved.
Metric: exact-match →Summaries, decisions, and tracked action items from meetings across the collaboration suite, permission-scoped per user.
Metric: action capture →The biggest downstream decision is where inference runs relative to your security boundary. Independent of that choice, every deployment ships with the same five-number constraint set and the same orchestration and observability stack: p95 latency, cost-per-call, throughput floor, accuracy floor, and recovery time objective.
AWS Bedrock, Azure OpenAI in Microsoft Foundry, or Google Vertex AI inside your own VPCs, so the data path stays inside your cloud boundary and your existing controls.
Most enterprise features →Self-hosted Llama or Mistral on vLLM for air-gapped or contractually on-prem workloads, with the same orchestration and eval stack as the cloud path.
vLLM, on-prem →A private model handles regulated workloads while a frontier model handles low-sensitivity tasks, routed by data classification, under one identical observability stack.
Routed by sensitivity →We model gross margin per AI feature first, because enterprise volume turns a small per-call miss into a seven-figure overrun. A feature that prices into negative contribution margin at expected usage gets re-scoped, not built. This is advisory work on how you fund or charge for the feature; it carries no Resourcifi service prices.
Works when AI usage roughly tracks headcount. Requires a predictable cost-per-seat, which forces tight per-call ceilings at enterprise scale.
Predictable usage →Budget or charge per ticket resolved, invoice processed, or document reviewed. Needs usage visibility so a business unit never gets a surprise bill.
Concentrated usage →A workable floor sits inside the platform price; heavy use is metered. Aligns gross margin with usage without scaring off light adopters.
Maturing programs →Enterprise AI touches systems of record, identity, and the audit trail, so security and governance are part of the build from the first sprint, never a checklist at the end.
The AI serving layer has to fit inside your existing certification boundary, not break it.
Procurement reviews the AI feature as part of the security review, asking about data flows, retention, and sub-processors.
How we build to it: we design the serving layer to fit your certification boundary, disclose sub-processors on day one, put DPAs in place, and log prompts and retrievals for audit.
Each use case has to be risk-classified before build, and high-risk systems need conformity documentation.
High-risk Annex III obligations apply from 2 December 2027 under the 2026 Digital Omnibus, with product-embedded systems from 2 August 2028.
How we build to it: we classify each use case (minimal, limited, high-risk, prohibited) before code, build the conformity-assessment package (risk management, data governance, technical documentation, transparency, human oversight, accuracy and cybersecurity evidence) ahead of the deadline, and do not build prohibited-tier use cases.
Govern, map, measure, and manage have to be demonstrable, not aspirational.
The four NIST functions map cleanly onto how a production AI feature is actually run.
How we build to it: we map the NIST functions to the five Production-First AI principles, so the eval suite, the constraint set, and the governance stack are the evidence that the framework is being followed.
Retrieval must be permission-aware, and the AI service cannot keep its own shadow user store.
The worst enterprise AI bug is an employee seeing a document their role never granted.
How we build to it: Okta or Microsoft Entra ID via SAML or OIDC with SCIM, permission-aware retrieval enforced at query time rather than after the model has seen the document, and DLP scanning with a four-layer governance stack (Guardrails.ai, validation pipelines, auto-retraining, and observability via LangSmith, Weights and Biases, Evidently AI, Prometheus and Grafana).
Some data cannot leave a region, or the building, and the architecture has to honor that.
Residency is an architecture decision, not a contract clause you can bolt on later.
How we build to it: per-region inference on cloud, full on-prem via vLLM where contractual, FedRAMP-aligned where required, and audit logs proving which model touched which record.
Every production deployment ships against five numbers defined before code is written.
At enterprise volume a small per-call miss becomes a seven-figure overrun, so the numbers are not optional.
How we build to it: p95 latency target, cost-per-call ceiling, throughput floor, accuracy floor on the reference dataset, and a recovery time objective, each instrumented from day one, with three rollback paths wired before any traffic flips.
We engineer to each of these. We do not claim certification on your behalf.
For context on why this matters: even as worldwide generative AI spending reaches an estimated 644 billion dollars in 2025, per Gartner, the share of companies abandoning most of their AI initiatives rose from 17 percent to 42 percent year over year, with the average organization scrapping 46 percent of its proof-of-concept projects before production, per S&P Global Market Intelligence. That gap between spend and production is the problem these enterprise AI solutions are built to close.
We map the use case, the systems of record it depends on, and the ISO, SOC 2, and EU AI Act surface, then set the deployment constraints first: the p95 latency target, the cost-per-call ceiling, and the accuracy floor, with a line-by-line estimate before you commit.
A senior AI engineer, named for the engagement before contracts are signed, assesses feasibility, the model strategy, the deployment topology, the identity and integration footprint across SAP, Salesforce, and Workday, and the governance plan, so the economics and the controls are clear before any code.
We sequence the features, each scoped and instrumented individually with its own latency budget, eval metric, and cost ceiling, so the 12-month plan is a set of shippable, measurable units rather than one big bet.
The feature ships in milestones wired into your systems of record over MuleSoft or Workato and your identity layer, and we stand up the three-layer eval suite as a first-class artifact: a reference dataset, an adversarial set, and a regression set where every incident becomes a permanent entry.
The eval suite runs on every deploy and on a schedule behind a feature flag, with three rollback paths wired before traffic flips, canary at 1, 10, 50, and 100 percent, tracing, the four-layer governance stack, and per-feature cost budgets switched on, so the first production deploy is observable and reversible.
We watch quality, latency, and spend on live traffic, fold drift and incidents back into the evals, and engineer the hand-off so your in-house team owns the model selection, the eval suite, the dashboards, and the run-book at the end.
Frontier models from Anthropic Claude, OpenAI, and Google Gemini on Bedrock, Azure OpenAI, or Vertex inside your VPC, plus open-weight Llama and Mistral self-hosted on vLLM for on-prem, with routing by data classification to balance quality, latency, and spend.
Claude, OpenAI, Llama, vLLM →Vector and hybrid search on pgvector, Pinecone, or Weaviate with Cohere Rerank, scoped to each user’s role at query time so retrieval never returns a document the source system would not.
pgvector, Cohere Rerank →LangGraph or Temporal with Apache Airflow or Prefect, integrated to SAP, Salesforce, ServiceNow, Workday, and NetSuite via MuleSoft or Workato, with Okta or Microsoft Entra ID for identity.
LangGraph, MuleSoft, Okta →A golden-set and LLM-judge eval harness wired into CI/CD, with a four-layer governance stack of named tools: Guardrails.ai for input and output validation, LangSmith for tracing, Weights & Biases for eval tracking, Evidently AI for drift, and Prometheus and Grafana for latency and cost, on AWS, GCP, or Azure.
Guardrails.ai, LangSmith, Evidently →A discovery call, then an AI assessment where a senior AI engineer is named for the engagement, not a faceless team, and which covers deployment topology, identity federation, and the governance plan, then roadmap, build, and deploy. For broader programs, see how we work with enterprise organizations and the engagement models we offer to enterprise teams.
One senior AI engineer to prove a single feature meets its deployment constraint set inside your security boundary.
Prove one feature →A small pod for a full feature ship, including evals, observability, system-of-record integration, and hand-off.
Ship to production →Multi-feature roadmaps and ongoing operate-mode work for teams shipping enterprise AI continuously.
Roadmaps and operate →We do not publish a named enterprise AI case study we cannot stand behind, so we will not invent one. What we can stand behind is the record: 200+ in-house experts across AI, data, and full-stack, 600+ projects delivered since 2017, a 95% repeat-client rate, and a 90-day median to a working build. A meaningful share of that work is Production Recovery, rebuilding AI features other vendors abandoned in proof of concept, where the model worked but the SAP connector did not, the audit log was an afterthought, or the CISO blocked rollout. The pattern holds: we scope the data, the eval criteria, and the governance surface first, deliver milestones you can see working, and ship behind the five-number constraint set so it holds under real load.
The questions CTOs, heads of AI, and IT governance leaders ask us on the first scoping call, answered straight.
Procurement is the long pole. Once the MSA is signed and access is provisioned, the 90-day median runs: the first 30 days for data access, identity federation, and the three-layer eval suite, then 60 days for build, canary, and hand-off. Pilots can prove a feature in 6 to 8 weeks. We do not ship an enterprise AI feature without evals running in CI.
Buyer choice. AWS Bedrock, Azure OpenAI in Microsoft Foundry, or Google Vertex AI inside your VPCs for public cloud; self-hosted open-weight models such as Llama or Mistral via vLLM for on-prem; hybrid where a private model handles regulated workloads and a frontier model handles low-sensitivity tasks. The orchestration, eval, and observability stack is identical in all three.
We classify each use case against the risk tiers before code is written. Under the 2026 Digital Omnibus, high-risk Annex III obligations apply from 2 December 2027 and product-embedded systems from 2 August 2028, so we build the conformity-assessment package (risk management, data governance, technical documentation, transparency, human oversight, and accuracy and cybersecurity evidence) to have you ready ahead of those dates. We do not build prohibited-tier use cases.
Okta and Microsoft Entra ID via SAML or OIDC with SCIM provisioning. The AI service does not maintain its own user store. Retrieval is permission-aware: an employee only sees what their role grants in the source system, enforced at query time, not after the model has seen the document.
We disclose every sub-processor before contracts: frontier model providers, vector stores, observability, iPaaS, and identity. Where a buyer rules a vendor out, self-hosted open-weight models such as Llama or Mistral via vLLM and pgvector in your VPC are the fully internal substitute. Mid-engagement changes require customer approval.
We treat ingested documents, tickets, and messages as untrusted and run them through a four-layer governance stack: model guardrails (Guardrails.ai), validation pipelines, auto-retraining where incidents become regression evals, and real-time observability (LangSmith, Weights and Biases, Evidently AI, Prometheus, Grafana). Content passes validation before it can influence an action.
We design for hand-off from week one. The pack: architecture diagrams, runbooks for 8 to 12 incident types, a prompt registry with rollback, an eval dashboard, a model upgrade SOP, a cost dashboard, a security checklist, and two weeks of paired on-call. Your in-house team owns model selection, the eval suite, observability, and the run-book at the end.
Enterprise AI solutions are production AI features, such as copilots, retrieval and search, service-desk and ops agents, and finance-ops automation, that are governed, secured, and integrated into the systems of record a large organization already runs, like SAP, Salesforce, Workday, and ServiceNow. They differ from a consumer chatbot in that they ship behind permission-aware retrieval, evaluations, guardrails, audit logs, and cost controls, and run on public cloud inside your VPC, on-prem, or hybrid. The hard part is rarely the model; it is closing the gap between a working demo and a feature that holds under real load and survives audit.
Build cost depends on scope, integration surface, and deployment topology, so we estimate line by line in discovery rather than quote a flat price. The larger cost driver over time is per-call inference at enterprise volume, which is why we model gross margin per feature before any code ships and re-scope anything that prices into negative contribution margin. We engage three ways: a 6 to 8 week pilot to prove one feature, a 12 to 16 week production build, or an ongoing enterprise pod, with a senior engineer named before you sign.
Streaming copilots grounded by enterprise data and governed by SSO and DLP, with evals and observability wired in from day one.
AI application development → AI agent developmentMulti-step, tool-using agents wired to your systems of record with manager-in-the-loop approval and the governance stack.
AI agent development → RAG developmentPermission-aware retrieval over enterprise corpora with audit logs on every inference.
RAG development → Custom LLM developmentOn-prem open-weight models or fine-tunes where residency and the economics justify going beyond a shared base model.
Custom LLM development → AI workflow automationAI combined with RPA via UiPath, Automation Anywhere, and Power Automate for finance-ops and back-office workflows.
AI workflow automation → AI consultingA named senior engineer to scope the use case, the SSO, RBAC and data-residency surface, and the deployment constraints before you commit to a build.
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