AI engineering — agents · RAG · evaluation

Build AI systems you can trust in production.

KraftSoft helps product teams design, evaluate, and ship production-ready AI agents, RAG systems, and the backend engineering that carries them into a real product.

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Project deliveryEmbedded engineersSenior-led

[ THE GAP ]

Building an AI demo is easy.
Making it reliable is not.

A prototype convinces a room. Production has to survive messy data, unusual questions, flaky tools, cost ceilings, and users who will not forgive a confident wrong answer. Four problems decide whether an AI feature ships or stalls.

  1. 01

    Reliable agent behavior and tool calling

    Agents pick the wrong tool, loop, or invent arguments. Reliability comes from constrained action spaces, validated inputs and outputs, explicit failure handling, and tested control flow — not from a longer prompt.

  2. 02

    RAG quality and grounded responses

    Retrieval returns something plausible, and the model fills the gaps. Fixing it means work on chunking, hybrid search, reranking, and permissions, then verifying that every claim traces back to a source.

  3. 03

    Automated evaluation and observability

    Without measurement, every prompt or model change is a guess. Automated evaluators, regression suites, and tracing turn quality into a number your team can watch and defend.

  4. 04

    Scalable backend and production integration

    Model calls are the small part. Streaming, queues, auth, tenancy, cost control, and deployment are what make an AI feature part of the product rather than a script beside it.

[ PROJECT-BASED AI ENGINEERING ]

Defined engagements with a technical outcome.

Each engagement starts from a specific problem and ends with something you can run, measure, and hand to your team. Scope and success criteria are agreed before work begins.

01

AI Evaluation Foundation

Typical duration — 3 to 5 weeks

The problem

You change a prompt, a model, or a retrieval setting and have no reliable way to tell whether quality improved or quietly regressed.

The outcome

A repeatable evaluation harness that scores your system on the dimensions that matter to your product, and runs on every change.

Deliverables

  • Evaluation strategy and quality criteria
  • Test scenarios and datasets built from real usage
  • Automated evaluators for groundedness, tool calls, output format, and business rules
  • Quality thresholds and pass/fail gates
  • Regression workflow wired into CI and monitoring
02

Agent & RAG Proof of Concept

Typical duration — 3 to 6 weeks

The problem

A use case looks promising, but there is no evidence it works on your data, inside your constraints, and within your latency and cost budget.

The outcome

A working prototype and a clear technical verdict: what is feasible now, what has to change, and what production would take.

Deliverables

  • Use case scoping and measurable success criteria
  • Working prototype running on your data
  • Architecture, model, and retrieval choices with rationale
  • Baseline quality measurements
  • Feasibility findings and a production roadmap
03

PoC-to-Production Sprint

Typical duration — 4 to 8 weeks

The problem

The prototype works in demos and breaks under real traffic, real users, and the edge cases nobody scripted.

The outcome

A hardened system that meets your reliability, security, and performance requirements, deployed into your environment.

Deliverables

  • Architecture and code audit with prioritized findings
  • Reliability hardening: retries, fallbacks, guardrails, failure paths
  • Security review: secrets, access control, data handling
  • Observability: tracing, structured logging, latency and cost metrics
  • Performance and cost tuning
  • Deployment pipeline and integration readiness
04

AI Backend & Integrations

Typical duration — from 4 weeks, scoped per milestone

The problem

The model logic is only part of the work. It has to live inside your product, your data, and your infrastructure without slowing the rest of the team down.

The outcome

Production backend services that carry AI functionality into your product, built to the standards the rest of your platform is held to.

Deliverables

  • APIs and orchestration services
  • Data and ingestion pipelines
  • Model and provider integrations
  • Streaming responses and long-running jobs
  • Authentication, authorization, and multi-tenancy
  • Cloud infrastructure, containers, and CI/CD

Scope, sequence, and duration are set after a technical conversation. Pricing follows scope — no fixed packages sold before the problem is understood.

[ TECHNICAL EXPERTISE ]

One connected stack, not a list of logos.

Agent behavior, retrieval quality, evaluation, and infrastructure are the same problem seen from different angles. We work across all of them so the seams do not become your problem.

Agentic AI

Agents that take actions you can predict, constrain, and debug.

  • AI agents
  • Tool calling
  • Multi-agent workflows
  • LangGraph
  • Planning & control loops
  • Structured outputs

RAG & Knowledge Systems

Retrieval that returns the right context, with answers traceable to sources.

  • Retrieval pipelines
  • Embeddings
  • Chunking
  • Hybrid search
  • Reranking
  • Citations
  • Grounded generation

Evaluation & Reliability

Quality expressed as measurements your team can track over time.

  • Automated evaluation
  • Regression testing
  • LLM-as-judge
  • Observability
  • Tracing
  • Guardrails

Backend Engineering

Services built to carry AI features at production load.

  • Python
  • FastAPI
  • REST APIs
  • Streaming
  • Async workers
  • Queues
  • PostgreSQL
  • Distributed systems

Cloud & Production

Deployment, scaling, and operations that hold up after launch.

  • AWS
  • Azure OpenAI
  • Docker
  • CI/CD
  • Infrastructure as code
  • Scalable deployment

[ HOW WE WORK ]

Short iterations, measurable checkpoints.

You work directly with the senior engineers doing the work. No account managers relaying questions, no delivery layer between the problem and the people solving it.

  1. 1

    Discovery

    Define the use case, the constraints, and what a successful outcome actually looks like in numbers.

  2. 2

    Architecture

    Design the solution and the evaluation strategy together, so quality is measurable from the first build.

  3. 3

    Build

    Deliver the proof of concept or production functionality in short iterations with working software at each step.

  4. 4

    Evaluate

    Measure quality, reliability, latency, and cost against the criteria agreed in discovery.

  5. 5

    Ship & support

    Integrate into your environment, hand over documentation and tooling, and continue development if you want us to.

[ ENGAGEMENT MODELS ]

Own the outcome, or strengthen your team.

[ WHY KRAFTSOFT ]

Specialists, not a general software vendor.

Built by engineers with hands-on experience delivering AI and backend systems in complex enterprise environments.

  • Senior-led delivery

    Your project is scoped and run by engineers who have shipped this kind of system before, not passed down a delivery chain.

  • Deep specialization

    Agents, retrieval, evaluation, and the backends around them. That focus is why the hard parts move faster.

  • AI and backend in one team

    Model behavior and production infrastructure get solved together, instead of split across two vendors and a handoff.

  • Measured quality

    Evaluation is part of the build, so quality is something you can see, track, and defend to stakeholders.

  • Flexible commitment

    Start with a defined project and scale into a dedicated team only once the direction is proven.

  • Direct access to engineers

    You talk to the people writing the code. Decisions get made in the conversation, not after it.

[ ABOUT ]

A senior-led AI engineering company.

KraftSoft is a US-based AI engineering company helping product teams turn promising AI ideas into dependable software. We combine practical experience in agentic AI, RAG, evaluation, backend systems, and cloud infrastructure.

The company runs as a compact senior core supported by a vetted international network of specialists. That structure exists for one reason: it makes it possible to assemble the right expertise for a specific problem instead of staffing a generic team and learning on your schedule.

Base
United States
Focus
AI agents, RAG, evaluation, AI backends
Delivery
Senior-led, remote collaboration
Clients
Product and engineering teams in the US and Europe

[ START A CONVERSATION ]

Have an AI initiative that needs to move forward?

Tell us what you are building, where you are blocked, and what a successful outcome looks like.

Discuss Your AI Project

Start with a focused technical conversation. No generic sales presentation.

ilia@kraftsoft.ai Remote collaboration · US & Europe