Bijanic
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From frontier AI to production systems.

We evaluate the strongest AI approaches against your workload, then build the software, data and infrastructure needed to run them reliably in production.

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What we build

Four kinds of systems.

  1. A queue of requests that people route by hand.

    Workflow and agent systems

    The model proposes the next action, code enforces your rules, and a person approves anything outside them - every step on record.

    1. InputRequest arrivesTicket, email or form
    2. StepGather contextThe sender's account, history and policy, from your systems
    3. ModelProposeReads the request, proposes one action with its reasons
    4. CheckWithin your rules?Limits you set, enforced in codeYes - automatic: Act. No - to a person: Approve.
    5. PersonApproveApprove, edit or reject on one screen
    6. StepActExecuted through the target system's API
    7. OutputAudit logRequest, proposal, decision and who made it
    Example system - not a client project.

  2. An archive of contracts, scans or recordings nobody can search.

    Document and multimodal intelligence

    Documents, images and audio in, structured records out - every field linked to the source it came from, and anything uncertain sent to a person.

    1. InputDocuments arriveEmail, upload, SFTP or scan
    2. ModelClassifyThe document type decides the schema
    3. ModelExtractEach field typed and linked to its source text
    4. StepValidateBusiness rules and existing records
    5. CheckAll fields certain?Per-field confidence and rule resultsYes - automatic: System of record. No - to a person: Review.
    6. PersonReviewEach field shown beside its source
    7. OutputSystem of recordWritten through its API, source attached
    Example system - not a client project.

  3. A desk answering the same questions from internal records.

    Search and assistants over your own data

    Answers drawn only from your records, each claim cited, limited to what the reader may see - and a plain "not found" when the records are silent.

    1. InputQuestionAsked inside your product by a signed-in user
    2. StepRetrieveKeyword and semantic search, only what this user may read
    3. ModelRankPassages ordered by relevance to the question
    4. CheckEnough evidence?A relevance threshold set from the evaluation setYes: Answer. No: Not found.
    5. ModelAnswerWritten only from the passages, each claim cited
    6. OutputNot foundSays so, and shows where it looked
    7. OutputTraceQuestion, passages and answer kept for evaluation
    Example system - not a client project.

  4. A list of customers, orders or assets that needs a priority.

    Predictive and decision systems

    A model learns from your own history what comes before a problem, code decides what is worth a person's time, and each signal arrives with its evidence.

    1. InputYour warehouseThe operational tables your team already models, every cycle
    2. StepReconcileScheduled, received and delivered quantities matched; breaks flagged in code
    3. ModelDetectChanges outside the expected range for this cycle, site and season
    4. CheckWorth a person?Size, persistence and your suppression rulesYes - to its owner: Explain and route. No - recorded only: Daily digest.
    5. ModelExplain and routeWhat changed and why, citing the rows and notices; a data error named as one
    6. PersonOwnerFrom your ownership table; acts on it or dismisses it, with a reason
    7. OutputOutcome recordedActed on or dismissed - the labels that tune the next threshold
    8. OutputDaily digestBelow the bar: recorded, no one interrupted
    Example system - not a client project.

Most work starts in one of two places: a prototype that has to reach production, or a clear opportunity with no system yet.

What we build changes with the problem. How we decide what goes into it does not.

Technically ambitious

Every serious candidate, measured on your workload.

The workload comes before the architecture: we start with how the decision or workflow works today, then decide where AI belongs and which approaches could change the outcome.

Those candidates - new models, techniques, retrieval, tuning - are benchmarked on representative data from your own workload, on the same cases for quality, latency, cost and safety. The production approach changes when the evidence supports it.

  1. InputNew approaches appearModels, techniques, retrieval and tuning, tracked as they are released
  2. StepCandidates shortlistedThe ones that could change your outcome, not the ones in the news
  3. StepRepresentative dataCases drawn from how the decision or workflow runs today
  4. StepBenchmarkQuality, latency, cost and safety, on the same cases for every candidate
  5. CheckBetter on the evidence?Against what runs in production today, by a margin agreed with youYes - adopted: Production selection. No - on file: Kept on file.
  6. OutputProduction selectionAdopted, with the evaluation kept for the next round
  7. OutputKept on fileThe result stays; the next release is measured against it
How an approach reaches production - the same path for every candidate.

Choosing the model is only the beginning.

Operationally disciplined

The model is only one part of the system.

A good model does not make a reliable product. Around it sit the software, the data, the permissions, the failure handling and the monitoring that production needs - designed in from the start, by the team that chose the model.

Your data stays where you decide: the system runs in your cloud or in a region we agree on, every model vendor is bound by contract, and nothing is trained on your data.

  1. ProductThe screens, approvals and hand-offs people actually use
  2. DataPipelines, records and the labelled sets the evaluation runs on
  3. AIThe model, the prompts or weights, the retrieval - chosen by the evidence
  4. IntegrationsYour systems of record, read and written through their APIs
  5. BackendThe code that enforces rules, limits and every action taken
  6. Security and accessWho may see what, enforced on every request; secrets and audit kept
  7. Cloud and operationsDeployment, scaling, cost and the on-call that keeps it up
  8. Observe and evaluateEvery answer traced; quality, cost and latency measured in production
  9. What production measures returns to the next evaluation round.
The anatomy of a production AI system. What production measures returns to the next evaluation - the loop on the right.
Commercially clear

A controlled path to production.

Each phase begins with agreed scope, acceptance criteria and price, and closes against those criteria before the next begins.

The scope is fixed before the price and the price is fixed per phase, before the work. Every gate is a written record - go, re-scope or stop - so nothing runs away in cost, scope or time.

  1. 01Discover
  2. Gate 1Success criteria definedSuccess criteria and the plan for the build, agreed in writing.
  3. 02Validate
  4. Gate 2Technical risk validatedThe technical risk measured on your data; the build priced.
  5. 03Build
  6. Gate 3Production readinessThe system meets the production criteria: evaluated, secured, observed, integrated, deployed.
  7. 04Run
No phase starts before the client accepts the previous gate.
The founder

The person making the decisions stays with the build.

Bijanic is founder-led: the founder makes the technical decisions and stays with the build through the release, so the person who chose the approach is the person who ships it. Who is assigned to the work is known before it begins, and what happens if he is unavailable is agreed in writing at the same time, not left to goodwill.

One team takes the product from prototype to production - no hand-off between an ML consultancy and a dev shop.

Milos BijanicFounder
Working together

We work best when the problem already matters.

  • A clear outcome: what has to be true once the system is live.
  • An owner on your side who can decide.
  • Access to the data and the systems the work touches.
  • A real intent to deploy.

If we don't believe we can materially improve the outcome, we'll say so early.

Bijanic - Engagement specificationRef. 2026-09
Scope
fixed before the price
Acceptance
criteria agreed before work starts
Price
fixed per phase
Ownership
named, from architecture through release
Code
yours on payment; our pre-existing foundation is licensed to you perpetually

Have something that needs to reach production?

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