What We Do / Generative AI & Intelligent Automation

GENERATIVE AI & INTELLIGENT AUTOMATION

Turn AI potential into practical business outcomes. Teceze helps enterprises move from AI experimentation to production with strategy, custom LLM engineering, RAG, agentic AI, intelligent workflow automation, enterprise knowledge systems, and responsible AI governance built around your data, processes, and business goals.

Book an AI value session
40–60%demand deflected via AI channels
−58%MTTR: 13.2 → 5.5 days
6.8Mtickets a year under AI-assisted ops
100%AI actions evidence-logged
Driven by
GenAI Innovation

Delivered through AI expertise, proven technology frameworks, and responsible GenAI practices to create secure, scalable, and intelligent solutions that evolve with your business.

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Nine services. One intelligence layer.

Individually consumable, designed to run as one. Pick a line to see what's inside.

SERVICE 01 OF 09

GenAI Strategy & Use-Case Ideation

Find the workflows where AI pays, price them against a measured baseline, and sequence them into a roadmap the board can hold you to.

  • Joint Value Discovery workshops
  • AI maturity & data readiness assessment
  • Use-case scoring: value vs feasibility
  • Baseline capture before any build
  • Build / buy / extend decisions
  • Target operating model & AI ownership
  • Business-case and run-cost modelling
  • Quarterly roadmap governance
Weeksnot quarters, to a costed roadmap
Baselinedvalue measured before build
Board-readybusiness case per use case

Platform ecosystem: Joint Value Discovery · context audit · value-vs-feasibility scoring · AI operating-model design

SERVICE 02 OF 09

Custom LLM Fine-Tuning

Models fine-tuned on your repositories, proprietary datasets and regulatory rules — so accuracy reflects how your business works, not an internet average.

  • Fine-tuning on private knowledge repositories
  • Regulatory and policy rule alignment
  • Evaluation harness & golden datasets
  • Domain benchmark scoring vs baseline
  • Private on-premise hosting option
  • In-boundary fine-tune option
  • Right-sized model selection
  • Drift monitoring & retraining cadence
In-boundaryfine-tuning where residency demands it
Evaluatedagainst your golden datasets
Right-sizedaccuracy per token of spend

Platform ecosystem: Azure OpenAI · Claude · Gemini · open-weight models · private hosted inference

SERVICE 03 OF 09

Context Engineering & RAG

Retrieval that stays honest at scale: high-signal token filtering that prevents context rot and the lost-in-the-middle decay behind most confidently wrong answers.

  • RAG architecture & retrieval pipelines
  • Embedding strategy & cached embeddings
  • Dynamic high-signal token filtering
  • Chunking & re-ranking design
  • Catalogue-scale retrieval without decay
  • Grounding & citation enforcement
  • Evaluation of answer faithfulness
  • Latency and cost tuning per query
Fewerhallucinations and lower token cost at once
Groundedanswers cite the source record
Scaleretrieval that holds at catalogue size

Platform ecosystem: Vector stores · hybrid search · re-rankers · cached embeddings · evaluation harnesses

SERVICE 04 OF 09

Agentic AI & Orchestration

Agents that take an objective, plan the steps, call your systems to execute them, and stop for a named human on exceptions.

  • Multi-agent orchestration & planning
  • MCP connectors: files, databases, shells, SaaS APIs
  • Schema validation on every agent output
  • Human-in-the-loop approval gates
  • Exception queues to named approvers
  • Sandboxed MVP agent on a real workload
  • Autonomy earned task by task
  • Non-repudiation via verified IAM identity
AgenticAI that completes work, not just answers
Gatedhuman approval before consequential action
MCPreaches systems without bespoke code

Platform ecosystem: Model Context Protocol · Copilot Studio · Azure OpenAI · Claude · Gemini · orchestration frameworks

SERVICE 05 OF 09

Intelligent Workflow Automation

The unglamorous majority of the value: document processing, approvals, reconciliation and service operations completed end to end.

  • Document ingestion & extraction at volume
  • Approval and exception workflow automation
  • RPA fulfilment (UiPath, Power Automate)
  • Self-heal and auto-remediation routines
  • Predictive routing & prioritisation
  • Straight-through processing design
  • Rework and error-rate instrumentation
  • Cycle-time reporting against baseline
40–60%of demand deflected via AI channels
−58%MTTR on a launched service (13.2 → 5.5 days)
6.8Mtickets a year under AI-assisted operations

Platform ecosystem: UiPath · Power Automate · ServiceNow · Now Assist · custom orchestration

SERVICE 06 OF 09

Enterprise Knowledge & Conversational AI

Fragmented knowledge turned into a conversational surface your people and customers actually trust — role-aware and permission-respecting.

  • Enterprise RAG on your knowledge estate
  • Virtual agents in Teams, portal and voice
  • Role-based access & permission inheritance
  • Agent-assist for analysts and advisors
  • Knowledge-gap detection from real questions
  • Multilingual and regional-language support
  • Deflection and containment measurement
  • Continuous content improvement loop
81%first-contact resolution with agent-assist
Multilingual24/7 across 10+ delivery centres
Deflectionmeasured, not assumed

Platform ecosystem: Copilot Studio · Kore.ai · Microsoft Teams · ServiceNow · enterprise search

SERVICE 07 OF 09

Guardrails, Shielding & AI Security

Real-time guardrail modules that inspect inputs and outputs natively — so the AI surface is something your security team can defend.

  • Prompt-injection and jailbreak blocking
  • Output inspection before it reaches a user
  • PII / PHI redaction at the guardrail layer
  • Data-leakage prevention on model calls
  • Adversarial fine-tuning bypass defence
  • Red-teaming and abuse-case testing
  • Secrets and credential isolation
  • Security review artefacts for sign-off
Both waysinputs and outputs inspected
Defensiblean AI surface security can sign off
Testedred-teamed before go-live

Platform ecosystem: Guardrail modules · content filters · DLP integration · Microsoft Defender · Entra ID

SERVICE 08 OF 09

Responsible AI Governance

The controls that let AI scale: policy, evidence, oversight and auditability built in from design rather than retrofitted after an incident.

  • AI policy & governance framework
  • Model and data lineage registers
  • Evidence log on every AI action
  • Human oversight & escalation design
  • Bias, fairness & safety evaluation
  • Residency, retention & redaction decisions
  • Audit and compliance evidence packs
  • Incident response for AI failure modes
100%AI actions written to the evidence log
Auditablecompliance packs on demand
Accountableevery action tied to an identity

Platform ecosystem: AI governance framework · model registry · evidence logging · SOC 2 aligned controls

SERVICE 09 OF 09

AI FinOps & Cost Engineering

Run-rate as a design parameter, not a surprise on the invoice — engineered before the first production token is spent.

  • Token filtering & prompt compression
  • Cached embeddings & response caching
  • Right-sized model routing per task
  • Dynamic budget sentry on runaway loops
  • Cost per resolution & per token telemetry
  • Chargeback and showback by business unit
  • Inference capacity & commitment planning
  • Cost regression testing on releases
Engineeredrun-cost designed, not discovered
Sentryrunaway loops terminated automatically
Per unitcost per resolution reported monthly

Platform ecosystem: Token telemetry · caching layers · model routing · cloud cost management · budget sentries

Powered by a Strong GenAI Ecosystem

Teceze GenAI Solutions Turn Generative AI into Business Advantage Use GenAI to automate knowledge intensive tasks, accelerate content and code creation, improve decision making, and enable employees to work smarter with AI powered insights and assistance.

Make GenAI Work for You
Microsoft
Slack
Google Drive
Outlook
Teams
Notion
ChatGPT
Zapier
Copilot
Skype
Cybersecurity tools

Your board approved an AI strategy. Your business got a pilot library.

An AI idea passes through a proof of concept, a demo, a security review, a data gap and a governance question — and every hand-off is a place where momentum, budget and business case get lost. The business doesn’t see a portfolio of experiments. It sees work that still isn’t done.

Use case
Proof of concept
Demo
Security review
Data gap
Governance question
Shelved
Disconnected pilots

Isolated tools and experiments that never touch the systems where work actually gets completed — so nothing compounds.

Generic models, no business context

Foundation models answer confidently from an internet average, then fail at the exact point your domain, schema or regulation gets specific.

Unowned risk

No guardrails, no approval gates, no audit trail — so security and compliance become the reason a working pilot never reaches production.

Demos measured, not outcomes

Innovation reported in prototypes rather than cost per resolution, cycle time or hours returned — and run-cost discovered on the invoice.

The result: real spend, visible activity, and a business case nobody can evidence — while the manual work carries on exactly as before.

The Operation Behind IT

AI at operating scale, measured in the open

40–60%
demand deflected via governed AI channels
58%
MTTR reduction: 13.2 → 5.5 days
6.8M
tickets a year under AI-assisted operations
81%
first-contact resolution with agent-assist
4.2M
end users in scope of AI-enabled services
97.2%
SLA met across managed operations
Multi-LLM
Azure OpenAI · Claude · Gemini · Copilot
100%
AI actions written to the evidence log
4,500+
technical professionals running the operations we automate
100%
AI actions written to the evidence log

GenAI Success Stories

Real World Generative AI Transformations

Explore how enterprises are using generative AI to streamline operations, enhance productivity, modernize workflows, and create smarter, more connected business experiences.

Explore All Case Studies

AI Cuts Clinical Document Handling by 60–70%

A hospital group used AI-assisted document intelligence and retrieval-augmented patient-service capabilities to streamline clinical and administrative workflows while maintaining human oversight, improving document handling speed by 60–70%, releasing approximately 30% of staff time, and reducing routine query response from hours to minutes.

View Case Study

AI-Driven Demand Forecasting Improves Accuracy by 18%, Reduces Stockouts by 25%

A retailer managing thousands of SKUs across stores, warehouses and online channels used AI-driven forecasting, replenishment intelligence and customer-service automation to improve priority-category forecast accuracy by 15–18%, reduce stockouts by 20–25%, and lower service handling time by 25–30%.

View Case Study

Why Teceze

Four reasons AI reaches production here

01.  We run the operations we automate

4,500+ technical professionals running managed operations at 6.8M tickets a year across 135+ countries. The processes our agents touch are processes we already operate so the automation is designed by the people who carry the SLA.

02.  Governed by design, not by exception

Guardrail modules on inputs and outputs, schema validation on what an agent may emit, human-in-the-loop approval on every consequential action, and each action cryptographically tied to a verified IAM identity for non-repudiation.

03.  Grounded in your data, inside your boundary

Domain-specific models fine-tuned on your repositories, context engineering that prevents lost-in-the-middle retrieval decay, and a hosting choice — private on-premise, in-boundary fine-tune or proxied public API — made before any model is selected.

04.  A layer over what you already bought

Model Context Protocol connectors reach your file stores, databases, shells and SaaS APIs without bespoke integration code, so the orchestration and guardrail layers sit above whichever models and clouds you have standardised on. No rip and replace.

Get In Touch

Find out which of your workflows an agent should own first

Start with a Joint Value Discovery and context audit: your data, your systems, your governance constraints and a roadmap ranked by measured value, not model novelty.

Talk to the AI practice team

A copilot answers when prompted; a person still does the work. An agent is given an objective, plans the steps, calls your enterprise systems to execute them, handles branches it did not expect, and stops for a human on exceptions. Copilots suit knowledge lookup and drafting. Agents suit repeatable, high-volume backend work where the value is a completed outcome. Most enterprises need both, sequenced: copilots to build trust and usage data, agents where the process is stable enough to hand over.

Four layers work together. Context engineering filters high-signal tokens so the model is not reasoning from degraded context. Guardrail modules inspect both inputs and outputs to block prompt injection and data leakage. Schema validation constrains what an agent is permitted to emit. And human-in-the-loop approval gates sit in front of every consequential action the agent proposes, a named person disposes, until the loop has earned autonomy on that specific task.

Only if you decide they should. The intelligence layer supports private on-premise hosted models, domain-specific models fine-tuned inside your boundary, and securely proxied public foundation APIs. Residency, retention and redaction are design decisions taken during the context audit — before any model is selected — and every agent action is cryptographically tied to a verified IAM identity for non-repudiation.

Joint Value Discovery and the context audit take weeks, not quarters, and a sandboxed MVP agent on a real workload typically follows inside the first quarter. Run cost is engineered rather than discovered: token filtering, cached embeddings and right-sized models cut inference spend, while a dynamic budget sentry terminates runaway loops before they drain an allocation so the run-rate is a design parameter rather than a surprise on the invoice.

Yes — the architecture is designed as a layer over existing enterprise cloud assets, not a replacement platform. Model Context Protocol connectors let agents reach your file stores, relational databases, shells and SaaS REST APIs without bespoke integration code, and the orchestration and guardrail layers sit above whichever models and clouds you have already standardised on.

The baseline is measured before anything is built — cost per resolution, cycle time, manual touches, error and rework rates during the context audit. Every agent then reports against that baseline in the same governance pack as the rest of the service. Published examples from our managed operations: 40–60% of demand deflected through AI channels, first-contact resolution at 81% with agent-assist, and mean time to resolution down 58% (13.2 to 5.5 days) within two months on a Nordic launch.

Security Strategy

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