AI & Agentic Engineering

AI agents that do real work —
not just chat about it.

Most enterprise AI stalls at the pilot. Ours ships: agents that quote, triage, reconcile, chase and report inside your actual systems — with the guardrails, audit trails and human controls that let compliance say yes.

6 wksIdea to production agent
80%Manual effort automated on target flows
100%Agent actions logged & reviewable
HITLHuman-in-the-loop on every critical step

What We Build

Agents for the work your teams wish they didn't do

01

Workflow Agents

Order processing, invoice matching, claims triage, vendor follow-ups — agents that execute multi-step business processes end to end, escalating only the exceptions.

02

Knowledge Agents (RAG)

Retrieval-augmented agents grounded in your contracts, SOPs, tickets and product data — answers with citations, not hallucinations.

03

Customer-Facing Copilots

Support and sales assistants that resolve, quote and upsell inside your brand experience, handing off to humans with full context.

04

Decision & Alert Agents

Agents that watch your data — stock thresholds, SLA risks, anomaly spikes — and act or alert per policies you define.

05

Document Intelligence

Extraction and validation across invoices, KYC documents, POs and contracts — pushed straight into your systems of record.

06

Agent Ops & Governance

Evaluation harnesses, cost and quality dashboards, prompt/version management and rollback — the platform to run agents responsibly at scale.

Built for Trust

Autonomy with a safety harness

Every agent we ship runs inside the same governance model as Nipige: typed actions, permission scopes, immutable logs and human approval gates where the stakes demand it.

  • Grounded in your dataAgents reason over governed enterprise data — never the open internet by default.
  • Typed, scoped actionsAn agent can only call the actions you grant, with the permissions you set.
  • Human-in-the-loop gatesPayments, contract terms, customer commitments — routed for approval, every time.
  • Measured like employeesAccuracy, throughput, cost-per-task and escalation rates on a live dashboard.
  • Model-flexibleBest-fit frontier or open models per task; swap without rebuilding the system.
  • Evaluation before autonomyAgents graduate from shadow mode → suggest mode → autonomous, on your evidence.
  • Full audit trailEvery input, decision and action logged and replayable for compliance review.
  • Kill switch includedPause any agent instantly; work routes back to human queues gracefully.

How We Deliver

From "could AI do this?" to measurable production

  1. AI opportunity assessment — week 1

    We map your workflows, score them for agent-fit (volume × rules × data readiness), and pick one with provable ROI. You get the scored map either way.

  2. Working agent on your data — weeks 2–3

    A functioning agent on a real slice of your data, run in shadow mode next to your team. You compare its output to reality before trusting it.

  3. Evaluation gate — week 4

    Accuracy, cost and escalation numbers reviewed together against agreed thresholds. It only graduates if the evidence says so.

  4. Production & scale — weeks 5–6+

    Governed rollout with approval gates, dashboards and training. Then we repeat the loop on the next workflow — each one faster than the last.

OpenAI & Anthropic modelsOpen-source LLMsLangChain / LlamaIndexVector searchAzure / AWS / GCP AIMCP & tool-callingEvaluation harnesses

One workflow. One agent. Two weeks. Free to see.

Bring your most repetitive process — we'll show you an agent running it in shadow mode.

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