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OutscalersHuman + AI Engineering

AI Solutions & Agents

AI that works inside your business, not beside it.

We design and build AI assistants, agents and document workflows that use your knowledge, respect your permissions and hand off to people when confidence is low.

Our approach
AI agent architectureAn AI agent draws on a knowledge base, documents and a database inside guardrails. Routine answers are returned with sources, while actions above a limit go to a person for approval before systems are updated.GUARDRAILS · PERMISSIONSKnowledge baseDocumentsDatabaseAgentAnswer + sourcesroutine requestHuman approvalabove set limitsUpdate CRMafter approval

The challenge

Why AI initiatives stall

  1. 01

    Knowledge is scattered

    Answers live across documents, tickets, wikis and inboxes. Teams spend time searching instead of deciding.

  2. 02

    Documents need manual handling

    Invoices, contracts and forms are read and re-keyed by hand, which is slow and error-prone.

  3. 03

    AI pilots stall before production

    Prototypes look promising, but lack evaluation, access control, monitoring and a clear owner.

Deliverables

AI capabilities we deliver

  • LLM integrations

    Language models connected to your products and internal tools through well-defined, testable APIs.

  • Conversational assistants

    Assistants for customers or staff, grounded in approved content and scoped to clear tasks.

  • AI agents

    Agents that call tools and APIs to complete multi-step tasks — with permissions, limits and approval steps.

  • Knowledge retrieval (RAG)

    Search and answer systems over your own content, with source references so answers can be checked.

  • Document intelligence

    Extraction and classification for structured and semi-structured documents, with review queues for exceptions.

  • Multimodal workflows

    Pipelines that combine text, images and scanned documents where your use case needs them.

Honest assessment

Where AI fits — and where it doesn't

Not every problem needs a language model. We'll tell you when a simpler solution is the better investment.

Often a good fit

  • Answering questions from a large body of internal documents
  • Extracting fields from documents that vary in layout
  • Classifying and routing requests by intent or urgency
  • Drafting responses that a person reviews before sending

Usually needs another approach

  • Decisions that must be fully explainable rule-by-rule
  • Calculations that need exact, deterministic results
  • High-impact actions with no human checkpoint
  • Problems a simple form or workflow would already solve

  • Permission-aware

    Assistants only see what the user is allowed to see.

  • Grounded answers

    Responses reference their sources so people can verify them.

  • Human escalation

    Low-confidence or sensitive cases route to a person.

  • Observable

    Inputs, outputs and feedback are logged for review and tuning.

Human + AI approach

Clear roles for people and AI

AI makes our engineers faster. It doesn't replace their judgment — and its output is always reviewed.

Engineers own

  • Define the use case, success criteria and acceptable risk
  • Choose models, data boundaries and access controls
  • Review outputs against real examples before release
  • Own escalation rules and production monitoring

AI assists

  • Drafts prompts, retrieval logic and integration code for engineers to refine
  • Generates evaluation cases and edge-case variations
  • Summarises model behaviour across test runs

AI suggestions are drafts. An engineer reviews, tests and approves before anything is merged.

Delivery process

How an engagement runs

A staged path from first review to a running system, so you see progress early and can adjust at every step.

  1. 1

    Step 01 / 05

    Use-case framing

    Identify where AI adds measurable value and where a simpler solution is better.

  2. 2

    Step 02 / 05

    Data & risk review

    Map sources, permissions and failure modes before any model is chosen.

  3. 3

    Step 03 / 05

    Prototype with evaluation

    Build a working slice and test it against real, representative examples.

  4. 4

    Step 04 / 05

    Harden & integrate

    Add guardrails, logging, fallbacks and integrations with your systems.

  5. 5

    Step 05 / 05

    Launch & monitor

    Release gradually, review feedback and tune behaviour with your team.

Technical capabilities

Tools and techniques

Models & APIs
Hosted LLM APIsOpen-weight modelsEmbeddingsFunction / tool calling
Retrieval
Vector searchHybrid searchChunking strategiesSource citation
Safety & quality
Evaluation suitesGuardrailsPII handlingHuman-in-the-loop review
Delivery
PythonNode.jsREST & webhooksObservability

Related services

Often combined with

$ outscalers start --project your-idea

Have an AI idea? Let's test whether it holds up.

Share your goals and constraints. We'll come back with practical options, a realistic plan and the questions worth asking.